In [2]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import keras
from keras import Sequential
import re
from keras import layers
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Input
from tensorflow.keras.optimizers import Adam
In [42]:
#将数据切割为hobbies, foods, and household,并再分为train, validation and test,已经不需要切割了,即不用运行了
def split_data(category):
df = pd.read_csv('train_with_price.csv')
category_data = df[df['cat_id'] == category]
train_df, temp_df = train_test_split(category_data, test_size=0.2, random_state=0)
test_df, val_df = train_test_split(temp_df, test_size=0.5, random_state=0)
train_df.to_csv(f'{category}_train_dataset.csv', index=False)
test_df.to_csv (f'{category}_test_dataset.csv', index=False)
val_df.to_csv (f'{category}_validation_dataset.csv', index=False)
def split_all_data():
category=['HOUSEHOLD','HOBBIES','FOODS']
for x in category:
split_data(x)
split_all_data()
C:\Users\a8090\AppData\Local\Temp\ipykernel_17932\1529081861.py:3: DtypeWarning: Columns (15,16) have mixed types. Specify dtype option on import or set low_memory=False.
df = pd.read_csv('train_with_price.csv')
C:\Users\a8090\AppData\Local\Temp\ipykernel_17932\1529081861.py:3: DtypeWarning: Columns (15,16) have mixed types. Specify dtype option on import or set low_memory=False.
df = pd.read_csv('train_with_price.csv')
C:\Users\a8090\AppData\Local\Temp\ipykernel_17932\1529081861.py:3: DtypeWarning: Columns (15,16) have mixed types. Specify dtype option on import or set low_memory=False.
df = pd.read_csv('train_with_price.csv')
In [3]:
#这里要手动把household改成foods和hobbies
train_data = pd.read_csv("HOUSEHOLD_train_dataset.csv")
validation_data = pd.read_csv("HOUSEHOLD_validation_dataset.csv")
testing_data = pd.read_csv("HOUSEHOLD_test_dataset.csv")
#预处理数据-去除不需要的栏,并将数据全部变为数字----
def preprocess_data(data):
data['item_id'] = data['item_id'].astype(str).apply(lambda x: ''.join(re.findall(r'\d+', x)))
data['dept_id'] = data['dept_id'].astype(str).apply(lambda x: ''.join(re.findall(r'\d+', x)))
data.drop(['cat_id'], axis=1, inplace=True)
data['store_id'] = data['store_id'].astype(str).apply(lambda x: ''.join(re.findall(r'\d+', x)))
data['state_id'] = data['state_id'].replace({'CA': 1, 'WI': 2, 'TX': 3})
data['d'] = data['d'].astype(str).apply(lambda x: ''.join(re.findall(r'\d+', x)))
data.drop(['date'], axis=1, inplace=True)
data['weekday'] = data['weekday'].replace({'Monday': 1, 'Tuesday': 2, 'Wednesday': 3, 'Thursday': 4, 'Friday': 5 , 'Saturday': 6, 'Sunday': 7})
data.drop(['event_name_1'], axis=1, inplace=True)
data.drop(['event_name_2'], axis=1, inplace=True)
data['event_type_1'] = data['event_type_1'].replace({'Religious': 1, 'Cultural': 1, 'National': 1, 'Sporting': 1})
data['event_type_2'] = data['event_type_2'].replace({'Religious': 1, 'Cultural': 1, 'National': 1, 'Sporting': 1})
data['event_type_1'] = data['event_type_1'].fillna(0) #将活动中空的数据变为0
data['event_type_2'] = data['event_type_2'].fillna(0)
data['event_type'] = data['event_type_1']+data['event_type_2']
data.drop(['event_type_1'], axis=1, inplace=True)
data.drop(['event_type_2'], axis=1, inplace=True)
data = data.dropna()
preprocess_data(train_data)
preprocess_data(validation_data)
preprocess_data(testing_data)
C:\Users\a8090\AppData\Local\Temp\ipykernel_10024\1590625379.py:12: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
data['state_id'] = data['state_id'].replace({'CA': 1, 'WI': 2, 'TX': 3})
C:\Users\a8090\AppData\Local\Temp\ipykernel_10024\1590625379.py:15: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
data['weekday'] = data['weekday'].replace({'Monday': 1, 'Tuesday': 2, 'Wednesday': 3, 'Thursday': 4, 'Friday': 5 , 'Saturday': 6, 'Sunday': 7})
C:\Users\a8090\AppData\Local\Temp\ipykernel_10024\1590625379.py:18: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
data['event_type_1'] = data['event_type_1'].replace({'Religious': 1, 'Cultural': 1, 'National': 1, 'Sporting': 1})
C:\Users\a8090\AppData\Local\Temp\ipykernel_10024\1590625379.py:19: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
data['event_type_2'] = data['event_type_2'].replace({'Religious': 1, 'Cultural': 1, 'National': 1, 'Sporting': 1})
C:\Users\a8090\AppData\Local\Temp\ipykernel_10024\1590625379.py:12: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
data['state_id'] = data['state_id'].replace({'CA': 1, 'WI': 2, 'TX': 3})
C:\Users\a8090\AppData\Local\Temp\ipykernel_10024\1590625379.py:15: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
data['weekday'] = data['weekday'].replace({'Monday': 1, 'Tuesday': 2, 'Wednesday': 3, 'Thursday': 4, 'Friday': 5 , 'Saturday': 6, 'Sunday': 7})
C:\Users\a8090\AppData\Local\Temp\ipykernel_10024\1590625379.py:18: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
data['event_type_1'] = data['event_type_1'].replace({'Religious': 1, 'Cultural': 1, 'National': 1, 'Sporting': 1})
C:\Users\a8090\AppData\Local\Temp\ipykernel_10024\1590625379.py:19: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
data['event_type_2'] = data['event_type_2'].replace({'Religious': 1, 'Cultural': 1, 'National': 1, 'Sporting': 1})
C:\Users\a8090\AppData\Local\Temp\ipykernel_10024\1590625379.py:12: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
data['state_id'] = data['state_id'].replace({'CA': 1, 'WI': 2, 'TX': 3})
C:\Users\a8090\AppData\Local\Temp\ipykernel_10024\1590625379.py:15: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
data['weekday'] = data['weekday'].replace({'Monday': 1, 'Tuesday': 2, 'Wednesday': 3, 'Thursday': 4, 'Friday': 5 , 'Saturday': 6, 'Sunday': 7})
C:\Users\a8090\AppData\Local\Temp\ipykernel_10024\1590625379.py:18: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
data['event_type_1'] = data['event_type_1'].replace({'Religious': 1, 'Cultural': 1, 'National': 1, 'Sporting': 1})
C:\Users\a8090\AppData\Local\Temp\ipykernel_10024\1590625379.py:19: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
data['event_type_2'] = data['event_type_2'].replace({'Religious': 1, 'Cultural': 1, 'National': 1, 'Sporting': 1})
In [4]:
train_data.head()
Out[4]:
| id | item_id | dept_id | store_id | state_id | d | num_sold | wm_yr_wk | weekday | month | year | snap | sell_price | event_type | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | HOUSEHOLD_1_225_WI_3_validation | 1225 | 1 | 3 | 2 | 1422 | 3 | 11447 | 6 | 12 | 2014 | 0 | 1.64 | 0.0 |
| 1 | HOUSEHOLD_1_523_TX_3_validation | 1523 | 1 | 3 | 3 | 918 | 0 | 11328 | 6 | 8 | 2013 | 1 | 20.53 | 0.0 |
| 2 | HOUSEHOLD_1_248_TX_3_validation | 1248 | 1 | 3 | 3 | 1070 | 1 | 11349 | 4 | 1 | 2014 | 0 | 8.97 | 0.0 |
| 3 | HOUSEHOLD_1_538_WI_1_validation | 1538 | 1 | 1 | 2 | 490 | 1 | 11218 | 5 | 6 | 2012 | 0 | 3.48 | 0.0 |
| 4 | HOUSEHOLD_2_261_TX_1_validation | 2261 | 2 | 1 | 3 | 383 | 0 | 11203 | 3 | 2 | 2012 | 1 | 6.97 | 0.0 |
The val_df and eval_df above are in the requested format for Kaggle competition predictions. In the end, we will merge these dataframes into a final consolidated file.
In [ ]:
In [35]:
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Input, Dropout
from tensorflow.keras.optimizers import Adam
from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error
import matplotlib.pyplot as plt
from tensorflow.keras.callbacks import EarlyStopping
def create_xy_data(df, pre_type = ""):
x_train_id = (df['id'] + "_" + pre_type).values
idx = np.unique(x_train_id, return_index=True)[1]
idx.sort()
x_train_id = x_train_id[idx]
y_train = df['num_sold'].values
df = df.drop(['id','num_sold','item_id', 'dept_id','wm_yr_wk', 'year'],axis=1)
return df, y_train, x_train_id
def create_x(df, pre_type=""):
x_train_id = (df['id'] + "_" + pre_type).values
idx = np.unique(x_train_id, return_index=True)[1]
idx.sort()
x_train_id = x_train_id[idx]
df = df.drop(['id','num_sold','item_id', 'dept_id', 'wm_yr_wk','year'],axis=1)
return df, x_train_id
def create_time_steps(length):
time_steps = []
for i in range(-length, 0, 1):
time_steps.append(i)
return time_steps
def show_plot(plot_data, delta, title):
labels = ['History', 'True Future', 'Model Prediction']
marker = ['.-', 'rx', 'go']
time_steps = create_time_steps(plot_data[0].shape[0])
if delta:
future = delta
else:
future = 0
plt.title(title)
for i, x in enumerate(plot_data):
if i:
plt.plot(future, plot_data[i], marker[i], markersize=10, label=labels[i])
else:
plt.plot(time_steps, plot_data[i].flatten(), marker[i], label=labels[i])
plt.legend()
plt.xlim([time_steps[0] - 1, (future + 2) * 2])
plt.xlabel('Time-Step')
return plt
LOOKBACK_MAX = 28 #28? 14?
LOOKBACK_ARR = np.array([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14])
y_val_pre_all = []
y_eval_pre_all = []
y_val_true_all = []
val_id_all = []
eval_id_all = []
SE_list = []
number_of_trained = 0
y_true_all = []
y_pred_all = []
unique_items = train_data['item_id'].unique()
target_scaler = MinMaxScaler(feature_range=(0, 1))
y_eval_output = []
# create a model for each product
while number_of_trained < 100 and number_of_trained < len(unique_items):
item = unique_items[number_of_trained]
print("-----------------------------------")
print("Current item is ", item)
# Filter data for the current item
item_train_data = train_data[train_data['item_id'] == item].reset_index(drop=True)
item_val_data = validation_data[validation_data['item_id'] == item].reset_index(drop=True)
item_eval_data = testing_data[testing_data['item_id'] == item].reset_index(drop=True)
item_train_data['state_id'] = item_train_data['state_id'].astype(str)
item_train_data['store_id'] = item_train_data['store_id'].astype(str)
item_val_data['state_id'] = item_val_data['state_id'].astype(str)
item_val_data['store_id'] = item_val_data['store_id'].astype(str)
item_eval_data['state_id'] = item_eval_data['state_id'].astype(str)
item_eval_data['store_id'] = item_eval_data['store_id'].astype(str)
# Since it is not reasonable to use data from different stores when extracting the previous 14 days
state_store_list = (item_train_data['state_id'] + '_' + item_train_data['store_id']).to_numpy()
val_state_store_list = (item_val_data['state_id'] + '_' + item_val_data['store_id']).to_numpy()
eval_state_store_list = (item_eval_data['state_id'] + '_' + item_eval_data['store_id']).to_numpy()
# Transform categorical data using One-hot-encoding
train_dummy = pd.get_dummies(item_train_data, columns=['store_id','state_id', 'weekday', 'snap'], drop_first=False)
train_columns = train_dummy.columns
val_dummy = pd.get_dummies(item_val_data, columns=['store_id','state_id', 'weekday', 'snap'], drop_first=False)
eval_dummy = pd.get_dummies(item_eval_data, columns=['store_id','state_id', 'weekday', 'snap'], drop_first=False)
print("Data types of train_dummy after one-hot encoding:")
print(train_dummy.dtypes)
missing_cols = set(train_dummy.columns) - set(eval_dummy.columns)
for col in missing_cols:
eval_dummy[col] = 0 # 补全缺失的列,并设为0
eval_dummy = eval_dummy[train_dummy.columns] # 保持列顺序一致
eval_dummy = eval_dummy.reindex(columns=train_dummy.columns, fill_value=0)
# Create dataframe
x_data, y_data, data_id = create_xy_data(train_dummy)
x_val, y_val, val_id = create_xy_data(val_dummy, pre_type="validation")
x_eval, eval_id = create_x(eval_dummy, pre_type="evaluation")
val_id_all.append(val_id)
eval_id_all.append(eval_id)
# Standardize price
scaler_x = StandardScaler()
x_data['sell_price'] = scaler_x.fit_transform(np.array(x_data['sell_price']).reshape(-1,1))
x_val['sell_price'] = scaler_x.transform(np.array(x_val['sell_price']).reshape(-1,1))
x_eval['sell_price'] = scaler_x.transform(np.array(x_eval['sell_price']).reshape(-1,1))
x_columns = x_data.columns
print(x_columns)
# Scale target variable
print(f"Original y_data range: min={np.min(y_data)}, max={np.max(y_data)}")
y_data_scaled = target_scaler.fit_transform(y_data.reshape(-1, 1)).flatten()
print(f"Scaled y_data range: min={np.min(y_data_scaled)}, max={np.max(y_data_scaled)}")
y_val_scaled = target_scaler.transform(y_val.reshape(-1, 1)).flatten()
print(f"Scaled y_val range: min={np.min(y_val_scaled)}, max={np.max(y_val_scaled)}")
x_time = []
y_time = []
x_val_time = []
y_val_time = []
x_eval_time = []
i = LOOKBACK_MAX
# Store-specific data extraction and handling
unique_stores = np.unique(state_store_list)
for store in unique_stores:
train_indices = np.where(state_store_list == store)[0]
val_indices = np.where(val_state_store_list == store)[0]
eval_indices = np.where(eval_state_store_list == store)[0]
store_x_data = x_data.iloc[train_indices].reset_index(drop=True)
store_y_data = y_data_scaled[train_indices]
store_x_val = x_val.iloc[val_indices].reset_index(drop=True)
store_y_val = y_val_scaled[val_indices]
store_x_eval = x_eval.iloc[eval_indices].reset_index(drop=True)
# Convert boolean columns to integers
bool_cols = ['store_id_1', 'store_id_2', 'store_id_3', 'store_id_4',
'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5', 'weekday_6', 'weekday_7',
'snap_0', 'snap_1']
for col in bool_cols:
if col in store_x_data.columns:
store_x_data[col] = store_x_data[col].astype(int)
if col in store_x_val.columns:
store_x_val[col] = store_x_val[col].astype(int)
if col in store_x_eval.columns:
store_x_eval[col] = store_x_eval[col].astype(int)
#看看有没有问题
for col in ['event_type_1', 'event_type_2']:
if col in store_x_data.columns:
store_x_data[col] = pd.to_numeric(store_x_data[col], errors='coerce')
if col in store_x_val.columns:
store_x_val[col] = pd.to_numeric(store_x_val[col], errors='coerce')
if col in store_x_eval.columns:
store_x_eval[col] = pd.to_numeric(store_x_eval[col], errors='coerce')
# Process training data for lookback
for i in range(LOOKBACK_MAX, len(store_x_data)):
lookback_data = []
for lb in LOOKBACK_ARR:
if i - lb >= 0:
lookback_data.append(store_x_data.iloc[i - lb].values.tolist())
else:
lookback_data.append([0] * store_x_data.shape[1])
x_time.append(lookback_data)
y_time.append(store_y_data[i])
# Process validation data for lookback
for i in range(LOOKBACK_MAX, len(store_x_val)):
lookback_data = []
for lb in LOOKBACK_ARR:
if i - lb >= 0:
lookback_data.append(store_x_val.iloc[i - lb].values.tolist())
else:
lookback_data.append([0] * store_x_val.shape[1])
x_val_time.append(lookback_data)
y_val_time.append(store_y_val[i])
for i in range(LOOKBACK_MAX, len(store_x_eval)):
lookback_data = []
for lb in LOOKBACK_ARR:
if i - lb >= 0:
lookback_data.append(store_x_eval.iloc[i - lb].values.tolist())
else:
lookback_data.append([0] * store_x_eval.shape[1])
x_eval_time.append(lookback_data)
x_time = np.array(x_time).astype(np.float32)
y_time = np.array(y_time).astype(np.float32)
x_val_time = np.array(x_val_time).astype(np.float32)
y_val_time = np.array(y_val_time).astype(np.float32)
x_eval_time= np.array(x_eval_time).astype(np.float32)
print(f"x_eval_time shape before reshape: {x_eval_time.shape}")
print(f"x_data.shape[1]: {x_data.shape[1]}")
print(f"LOOKBACK_ARR.shape[0]: {LOOKBACK_ARR.shape[0]}")
print(f"item_eval_data.shape: {item_eval_data.shape}")
unique_stores = np.unique(eval_state_store_list)
for store in unique_stores:
eval_indices = np.where(eval_state_store_list == store)[0]
store_x_eval = x_eval.iloc[eval_indices].reset_index(drop=True)
print(f"store_x_eval.shape: {store_x_eval.shape}")
x_time = x_time.reshape((x_time.shape[0], LOOKBACK_ARR.shape[0], x_data.shape[1]))
x_val_time = x_val_time.reshape((x_val_time.shape[0], LOOKBACK_ARR.shape[0], x_data.shape[1]))
x_eval_time = x_eval_time.reshape((x_eval_time.shape[0], LOOKBACK_ARR.shape[0], x_eval.shape[1]))
model = Sequential()
model.add(Input(shape=(LOOKBACK_ARR.shape[0], x_data.shape[1])))
model.add(LSTM(256, activation='relu', return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(256, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(1))
model.compile(optimizer=Adam(learning_rate=0.001), loss='mse')
model.summary()
number_of_trained +=1
# Early stopping
early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)
history = model.fit(x = x_time,
y = y_time,
epochs=100,
shuffle=True,
batch_size=128,
validation_split = 0.1,
verbose=1,
callbacks=[early_stopping])
# Predict
y_val_pre_scaled = model.predict(x_val_time).reshape(-1,)
y_val_pre = target_scaler.inverse_transform(y_val_pre_scaled.reshape(-1, 1)).flatten()
y_val_true_original = target_scaler.inverse_transform(y_val_time.reshape(-1, 1)).flatten()
y_val_pre = np.maximum(0, y_val_pre)
y_val_pre_all.extend(y_val_pre)
y_val_true_all.extend(y_val_true_original)
print("Sample raw predictions (after inverse transform and clipping):", y_val_pre[:5])
MSE = mean_squared_error(y_val_true_original, y_val_pre)
SE_list.append(MSE*len(y_val_pre))
print("RMSE = ", np.sqrt(MSE))
r2 = r2_score(y_val_true_original, y_val_pre)
print(f"Validation R-squared for item {item}: {r2}")
y_eval_pre_scaled = model.predict(x_eval_time).reshape(-1,)
y_eval_pre = target_scaler.inverse_transform(y_eval_pre_scaled.reshape(-1, 1)).flatten()
y_eval_pre = np.maximum(0, y_eval_pre)
if len(y_eval_pre) >= 28:
y_eval_output.append(y_eval_pre[-28:])
else:
padding = np.zeros(28 - len(y_eval_pre))
y_eval_output.append(np.concatenate([y_eval_pre, padding]))
print(f"Predicted y_val range (after inverse transform and clipping): min={np.min(y_val_pre)}, max={np.max(y_val_pre)}")
print(f"True y_val range (after inverse transform): min={np.min(y_val_true_original)}, max={np.max(y_val_true_original)}")
#可视化图表-折线图
plot_idx = np.random.randint(0, len(x_val_time))
plot_sample = x_val_time[plot_idx]
true_value_scaled = y_val_time[plot_idx]
prediction_scaled = y_val_pre_scaled[plot_idx]
true_value_original = target_scaler.inverse_transform(np.array([true_value_scaled]).reshape(-1, 1)).flatten()[0]
prediction_original = target_scaler.inverse_transform(np.array([prediction_scaled]).reshape(-1, 1)).flatten()[0]
plot_data = [
plot_sample[:, -1],
np.array([true_value_original]),
np.array([prediction_original])]
plt = show_plot(plot_data, delta=1, title=f'Item {item} Prediction Example')
plt.show()
from sklearn.metrics import r2_score, mean_absolute_error
#给出总体的结果
overall_mse = mean_squared_error(y_val_true_all, y_val_pre_all)
overall_rmse = np.sqrt(overall_mse)
overall_r2 = r2_score(y_val_true_all, y_val_pre_all)
overall_mae = mean_absolute_error(y_val_true_all, y_val_pre_all)
print("-----------------------------------")
print("Overall Validation MSE:", overall_mse)
print("Overall Validation RMSE:", overall_rmse)
print("Overall Validation MAE:", overall_mae)
print("Overall Validation R-squared:", overall_r2)
-----------------------------------
Current item is 1225
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=19
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.7894736842105263
x_eval_time shape before reshape: (1653, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1933, 14)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (171, 20)
store_x_eval.shape: (174, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (216, 20)
store_x_eval.shape: (169, 20)
Model: "sequential_76"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_153 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_58 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_154 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_59 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_75 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 53ms/step - loss: 18924.4102 - val_loss: 1972.5114 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 3934.3872 - val_loss: 43.5837 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 201.9400 - val_loss: 16.4313 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 107.3859 - val_loss: 15.2957 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 81.3130 - val_loss: 8.4849 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 58.3467 - val_loss: 5.5222 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 34.1280 - val_loss: 3.1466 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 22.7189 - val_loss: 1.6834 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 18.6499 - val_loss: 1.6412 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 14.3320 - val_loss: 1.2821 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 52ms/step - loss: 11.1413 - val_loss: 0.9112 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 8.2863 - val_loss: 1.4349 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 5.9132 - val_loss: 0.9281 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 4.5225 - val_loss: 0.1967 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 3.8019 - val_loss: 0.1853 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 3.4947 - val_loss: 0.6657 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 2.3398 - val_loss: 0.1178 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 52ms/step - loss: 2.0841 - val_loss: 0.1700 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 1.9809 - val_loss: 0.2252 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 1.6044 - val_loss: 0.4580 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 1.0813 - val_loss: 3.5423 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 1.5756 - val_loss: 0.0879 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 1.2094 - val_loss: 0.0547 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.7845 - val_loss: 0.8424 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 52ms/step - loss: 1.0836 - val_loss: 0.8386 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 1.0909 - val_loss: 0.3903 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.5830 - val_loss: 0.0453 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.6938 - val_loss: 0.4115 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.6884 - val_loss: 1.0772 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.6898 - val_loss: 1.6949 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.9349 - val_loss: 0.0234 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.4164 - val_loss: 0.2498 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.5154 - val_loss: 0.5085 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.9582 - val_loss: 0.0206 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.3980 - val_loss: 0.4075 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 50ms/step - loss: 0.4681 - val_loss: 0.1553 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.5845 - val_loss: 0.0316 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.2374 - val_loss: 7.0152 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 1.8323 - val_loss: 0.0333 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.1305 - val_loss: 0.7829 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.5019 - val_loss: 0.0767 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.6843 - val_loss: 0.1282 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.7374 - val_loss: 0.0664 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.2720 - val_loss: 0.0113 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.2611 - val_loss: 0.0128 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.3468 - val_loss: 0.6177 Epoch 47/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.1939 - val_loss: 0.1886 Epoch 48/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.3647 - val_loss: 0.1296 Epoch 49/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.5314 - val_loss: 0.5724 Epoch 50/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.2617 - val_loss: 0.7809 Epoch 51/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.8595 - val_loss: 0.2279 Epoch 52/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.3186 - val_loss: 0.0122 Epoch 53/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.1582 - val_loss: 0.0532 Epoch 54/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 5s 51ms/step - loss: 0.1202 - val_loss: 0.8902 53/53 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step Sample raw predictions (after inverse transform and clipping): [2.2722342 2.019139 2.2932293 0. 0.7174268] RMSE = 2.5319178 Validation R-squared for item 1225: -0.0014235973358154297 52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=4.185342311859131 True y_val range (after inverse transform): min=0.0, max=15.000000953674316
-----------------------------------
Current item is 1523
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=5
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.2000000000000002
x_eval_time shape before reshape: (1559, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1839, 14)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (167, 20)
store_x_eval.shape: (180, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (210, 20)
store_x_eval.shape: (160, 20)
store_x_eval.shape: (178, 20)
Model: "sequential_77"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_155 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_60 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_156 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_61 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_76 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 7s 53ms/step - loss: 14375.5762 - val_loss: 675.8505 Epoch 2/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 996.1748 - val_loss: 17.9162 Epoch 3/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 112.1268 - val_loss: 2.4032 Epoch 4/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 48.4905 - val_loss: 1.3057 Epoch 5/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 32.1874 - val_loss: 0.5965 Epoch 6/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 20.3652 - val_loss: 0.4104 Epoch 7/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 11.1131 - val_loss: 0.2977 Epoch 8/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 6.4639 - val_loss: 0.1202 Epoch 9/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 4.1530 - val_loss: 0.0690 Epoch 10/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 2.6970 - val_loss: 0.0471 Epoch 11/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 1.6188 - val_loss: 0.0391 Epoch 12/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.1543 - val_loss: 0.0540 Epoch 13/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 1.3431 - val_loss: 0.0230 Epoch 14/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 1.0959 - val_loss: 0.0468 Epoch 15/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.9224 - val_loss: 0.0157 Epoch 16/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.6580 - val_loss: 0.0145 Epoch 17/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.5738 - val_loss: 0.0147 Epoch 18/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.5274 - val_loss: 0.0110 Epoch 19/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.4620 - val_loss: 0.0103 Epoch 20/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.3637 - val_loss: 0.0230 Epoch 21/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.3407 - val_loss: 0.0221 Epoch 22/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.2175 - val_loss: 0.0121 Epoch 23/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.1130 - val_loss: 0.0056 Epoch 24/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1146 - val_loss: 0.0054 Epoch 25/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0541 - val_loss: 0.0050 Epoch 26/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0573 - val_loss: 0.0066 Epoch 27/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0534 - val_loss: 0.0047 Epoch 28/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0540 - val_loss: 0.0048 Epoch 29/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0427 - val_loss: 0.0049 Epoch 30/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0764 - val_loss: 0.0051 Epoch 31/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0483 - val_loss: 0.0051 Epoch 32/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0648 - val_loss: 0.0047 Epoch 33/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0246 - val_loss: 0.0048 Epoch 34/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0386 - val_loss: 0.0118 Epoch 35/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 12.8230 - val_loss: 0.4015 Epoch 36/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 3.0136 - val_loss: 0.0045 Epoch 37/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.1632 - val_loss: 0.0050 Epoch 38/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.1161 - val_loss: 0.0044 Epoch 39/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0579 - val_loss: 0.0043 Epoch 40/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0582 - val_loss: 0.0045 Epoch 41/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0239 - val_loss: 0.0044 Epoch 42/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0651 - val_loss: 0.0043 Epoch 43/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0188 - val_loss: 0.0043 Epoch 44/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0175 - val_loss: 0.0043 Epoch 45/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0541 - val_loss: 0.0043 Epoch 46/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0286 - val_loss: 0.0043 Epoch 47/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1814 - val_loss: 0.0042 Epoch 48/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0188 - val_loss: 0.0045 Epoch 49/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0136 - val_loss: 0.0043 Epoch 50/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0096 - val_loss: 0.0043 Epoch 51/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0328 - val_loss: 0.0043 Epoch 52/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0077 - val_loss: 0.0043 Epoch 53/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0100 - val_loss: 0.0043 Epoch 54/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0372 - val_loss: 0.0043 Epoch 55/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0153 - val_loss: 0.0043 Epoch 56/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0153 - val_loss: 0.0043 Epoch 57/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0084 - val_loss: 0.0043 51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step Sample raw predictions (after inverse transform and clipping): [0. 0.06638031 0. 0.07186427 0.07335408] RMSE = 0.39316848 Validation R-squared for item 1523: 0.008431077003479004 49/49 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=0.07957331091165543 True y_val range (after inverse transform): min=0.0, max=6.0
-----------------------------------
Current item is 1248
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=8
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.875
x_eval_time shape before reshape: (1660, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1940, 14)
store_x_eval.shape: (177, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (207, 20)
store_x_eval.shape: (206, 20)
Model: "sequential_78"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_157 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_62 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_158 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_63 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_77 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 54ms/step - loss: 24026.9473 - val_loss: 793.8290 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 2744.6355 - val_loss: 227.1237 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 855.1089 - val_loss: 165.0681 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 477.7781 - val_loss: 71.7934 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 172.5361 - val_loss: 30.8414 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 64.1932 - val_loss: 13.4713 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 19.3592 - val_loss: 0.5412 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 3.0346 - val_loss: 0.3821 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.9311 - val_loss: 0.2638 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 1.4099 - val_loss: 0.2623 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.0135 - val_loss: 0.2730 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.9153 - val_loss: 0.1904 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.6460 - val_loss: 0.4742 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.6965 - val_loss: 0.1564 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.5279 - val_loss: 0.2865 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.6146 - val_loss: 0.1373 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.4497 - val_loss: 0.1310 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.4309 - val_loss: 0.2612 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.4116 - val_loss: 0.2119 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.4244 - val_loss: 0.1989 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.3667 - val_loss: 0.4144 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.4053 - val_loss: 0.2122 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.4487 - val_loss: 0.5739 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.4119 - val_loss: 0.0968 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.3300 - val_loss: 0.0961 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.3216 - val_loss: 0.0852 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.4113 - val_loss: 0.0798 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.2622 - val_loss: 0.2852 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.3576 - val_loss: 0.1758 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.2142 - val_loss: 0.1538 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.2382 - val_loss: 0.0697 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.2233 - val_loss: 0.2451 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.2406 - val_loss: 0.1461 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1955 - val_loss: 0.2066 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.2139 - val_loss: 0.0686 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1908 - val_loss: 0.0808 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1678 - val_loss: 0.0456 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1854 - val_loss: 0.0457 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1759 - val_loss: 0.0448 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1561 - val_loss: 0.1248 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1711 - val_loss: 0.3891 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.1809 - val_loss: 0.0876 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1344 - val_loss: 0.0345 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1402 - val_loss: 0.1005 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1627 - val_loss: 0.0338 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1302 - val_loss: 0.0864 Epoch 47/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0980 - val_loss: 0.2093 Epoch 48/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1356 - val_loss: 0.1138 Epoch 49/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0979 - val_loss: 0.2162 Epoch 50/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1157 - val_loss: 0.0261 Epoch 51/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0930 - val_loss: 0.0217 Epoch 52/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0704 - val_loss: 0.0239 Epoch 53/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0611 - val_loss: 0.0202 Epoch 54/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0737 - val_loss: 0.0196 Epoch 55/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0581 - val_loss: 0.0177 Epoch 56/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 0.0690 - val_loss: 0.0359 Epoch 57/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0670 - val_loss: 0.0277 Epoch 58/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1516 - val_loss: 0.0155 Epoch 59/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0660 - val_loss: 0.0209 Epoch 60/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0509 - val_loss: 0.0541 Epoch 61/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0466 - val_loss: 0.0183 Epoch 62/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0356 - val_loss: 0.0173 Epoch 63/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0775 - val_loss: 0.0128 Epoch 64/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0365 - val_loss: 0.0122 Epoch 65/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0307 - val_loss: 0.0262 Epoch 66/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0337 - val_loss: 0.0597 Epoch 67/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0475 - val_loss: 0.0115 Epoch 68/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0397 - val_loss: 0.0183 Epoch 69/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0246 - val_loss: 0.0311 Epoch 70/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0360 - val_loss: 0.0124 Epoch 71/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0324 - val_loss: 0.0105 Epoch 72/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0438 - val_loss: 0.0107 Epoch 73/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0254 - val_loss: 0.0389 Epoch 74/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0431 - val_loss: 0.0109 Epoch 75/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0488 - val_loss: 0.0226 Epoch 76/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0607 - val_loss: 0.0149 Epoch 77/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0392 - val_loss: 0.0115 Epoch 78/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.0589 - val_loss: 0.0888 Epoch 79/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 0.0521 - val_loss: 0.0112 Epoch 80/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0337 - val_loss: 0.0117 Epoch 81/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0159 - val_loss: 0.0112 49/49 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step Sample raw predictions (after inverse transform and clipping): [0.63034356 0.5797154 0.54396343 0.6172495 0.6111356 ] RMSE = 0.9929343 Validation R-squared for item 1248: 0.03697305917739868 52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=1.031778335571289 True y_val range (after inverse transform): min=0.0, max=7.0
-----------------------------------
Current item is 1538
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=14
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.8571428571428571
x_eval_time shape before reshape: (1623, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1903, 14)
store_x_eval.shape: (172, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (213, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (186, 20)
Model: "sequential_79"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_159 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_64 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_160 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_65 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_78 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 55ms/step - loss: 41959.0703 - val_loss: 2031.2432 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 6123.6948 - val_loss: 969.4879 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1905.3384 - val_loss: 406.2067 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 984.2078 - val_loss: 456.9229 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 721.8736 - val_loss: 34.0741 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 146.9604 - val_loss: 12.6738 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 52.8940 - val_loss: 15.0009 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 36.6514 - val_loss: 4.2288 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 20.1532 - val_loss: 2.2451 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 13.7341 - val_loss: 2.9157 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 15.5807 - val_loss: 1.3909 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 9.9564 - val_loss: 1.0309 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 8.0590 - val_loss: 1.4787 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 7.4627 - val_loss: 1.5446 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 8.0416 - val_loss: 0.7313 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 5.2434 - val_loss: 0.5404 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 4.7093 - val_loss: 0.4622 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 4.5738 - val_loss: 0.3846 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 4.1706 - val_loss: 1.7314 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 3.9720 - val_loss: 0.4521 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 3.6575 - val_loss: 1.5607 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 3.1649 - val_loss: 0.2225 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 3.4060 - val_loss: 0.1982 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 2.7193 - val_loss: 0.2320 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 2.9729 - val_loss: 0.1875 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 2.6760 - val_loss: 1.4319 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 2.7769 - val_loss: 0.1552 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 2.2377 - val_loss: 0.3007 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 2.1522 - val_loss: 0.1516 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 2.1330 - val_loss: 0.3162 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.9306 - val_loss: 0.1074 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 2.3067 - val_loss: 0.4346 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 1.8774 - val_loss: 1.5859 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 2.0826 - val_loss: 0.4320 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.8505 - val_loss: 0.6556 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 52ms/step - loss: 1.6403 - val_loss: 0.2080 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.6060 - val_loss: 0.5062 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.4251 - val_loss: 0.1631 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.1662 - val_loss: 0.1030 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.3284 - val_loss: 0.0737 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.2199 - val_loss: 0.5149 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.5666 - val_loss: 0.6006 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.2929 - val_loss: 0.0626 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.1791 - val_loss: 0.3582 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.4624 - val_loss: 0.0588 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.6098 - val_loss: 0.4639 Epoch 47/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.3828 - val_loss: 0.1437 Epoch 48/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.2103 - val_loss: 0.0691 Epoch 49/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 1.0930 - val_loss: 0.1633 Epoch 50/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.3568 - val_loss: 0.1975 Epoch 51/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.1776 - val_loss: 0.1003 Epoch 52/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.1255 - val_loss: 0.5911 Epoch 53/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.0996 - val_loss: 0.1483 Epoch 54/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.9968 - val_loss: 0.1230 Epoch 55/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.9849 - val_loss: 0.2787 52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step Sample raw predictions (after inverse transform and clipping): [2.5119102 2.2433257 4.408477 1.2089206 2.584147 ] RMSE = 2.817949 Validation R-squared for item 1538: -3.216616630554199 51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 11ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=11.389867782592773 True y_val range (after inverse transform): min=0.0, max=12.0
-----------------------------------
Current item is 2261
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=6
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.0
x_eval_time shape before reshape: (1609, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1889, 14)
store_x_eval.shape: (213, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (175, 20)
store_x_eval.shape: (152, 20)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (202, 20)
Model: "sequential_80"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_161 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_66 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_162 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_67 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_79 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 7s 56ms/step - loss: 18356.6504 - val_loss: 1372.9249 Epoch 2/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 2096.1243 - val_loss: 151.5039 Epoch 3/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 265.0948 - val_loss: 101.6968 Epoch 4/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 266.2577 - val_loss: 24.5295 Epoch 5/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 101.3274 - val_loss: 19.5663 Epoch 6/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 77.3651 - val_loss: 10.7925 Epoch 7/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 42.1672 - val_loss: 9.7049 Epoch 8/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 25.9676 - val_loss: 8.7867 Epoch 9/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 16.0472 - val_loss: 4.5568 Epoch 10/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 12.3033 - val_loss: 7.1910 Epoch 11/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 10.7089 - val_loss: 3.0900 Epoch 12/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 8.4570 - val_loss: 1.6945 Epoch 13/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 6.4940 - val_loss: 1.2182 Epoch 14/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 9.6205 - val_loss: 7.5517 Epoch 15/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 10.8824 - val_loss: 1.5250 Epoch 16/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 6.3326 - val_loss: 1.2789 Epoch 17/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 4.9424 - val_loss: 0.4380 Epoch 18/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 2.6740 - val_loss: 0.3503 Epoch 19/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.4201 - val_loss: 0.2654 Epoch 20/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 0.9481 - val_loss: 1.9325 Epoch 21/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 4.8880 - val_loss: 0.4248 Epoch 22/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.9355 - val_loss: 0.2256 Epoch 23/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 1.0352 - val_loss: 0.1608 Epoch 24/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.8741 - val_loss: 0.1429 Epoch 25/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 0.3140 - val_loss: 0.0766 Epoch 26/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 0.1957 - val_loss: 1.3173 Epoch 27/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 0.4619 - val_loss: 0.0341 Epoch 28/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1493 - val_loss: 0.0249 Epoch 29/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1847 - val_loss: 0.2003 Epoch 30/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1421 - val_loss: 0.0354 Epoch 31/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1015 - val_loss: 0.0720 Epoch 32/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 0.0748 - val_loss: 0.0163 Epoch 33/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1100 - val_loss: 0.0272 Epoch 34/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 0.0928 - val_loss: 0.0675 Epoch 35/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1828 - val_loss: 0.0134 Epoch 36/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0890 - val_loss: 0.0953 Epoch 37/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.1172 - val_loss: 0.0225 Epoch 38/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 0.1955 - val_loss: 0.0995 Epoch 39/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 0.1436 - val_loss: 0.2400 Epoch 40/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 0.2166 - val_loss: 0.0161 Epoch 41/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0769 - val_loss: 0.0164 Epoch 42/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0889 - val_loss: 0.0125 Epoch 43/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.0797 - val_loss: 0.0812 Epoch 44/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 0.4213 - val_loss: 0.0300 Epoch 45/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.2132 - val_loss: 0.0223 Epoch 46/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 0.0466 - val_loss: 0.0190 Epoch 47/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 0.0416 - val_loss: 0.0355 Epoch 48/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 0.0323 - val_loss: 0.0599 Epoch 49/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 0.0400 - val_loss: 0.0586 Epoch 50/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.0558 - val_loss: 0.0198 Epoch 51/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 54ms/step - loss: 0.3228 - val_loss: 0.2836 Epoch 52/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 6s 53ms/step - loss: 0.7146 - val_loss: 0.0880 50/50 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step Sample raw predictions (after inverse transform and clipping): [0. 0.01364818 0.03765383 0.18379766 0. ] RMSE = 0.7404233 Validation R-squared for item 2261: -0.5214437246322632 51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=11.389328956604004 True y_val range (after inverse transform): min=0.0, max=6.0
-----------------------------------
Current item is 2103
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=8
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.75
x_eval_time shape before reshape: (1593, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1873, 14)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (176, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (168, 20)
store_x_eval.shape: (215, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (180, 20)
Model: "sequential_81"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_163 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_68 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_164 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_69 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_80 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 57ms/step - loss: 23160.5195 - val_loss: 3007.2212 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 4080.3223 - val_loss: 106.1965 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 342.3623 - val_loss: 29.9898 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 118.4812 - val_loss: 14.5527 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 77.4847 - val_loss: 2.6997 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 12.0562 - val_loss: 2.8802 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 10.6096 - val_loss: 0.1044 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 1.0678 - val_loss: 0.0187 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.5533 - val_loss: 0.0227 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.3805 - val_loss: 0.0251 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.4120 - val_loss: 0.0463 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.2318 - val_loss: 0.0227 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.1730 - val_loss: 0.0170 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.1774 - val_loss: 0.0119 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - loss: 0.1156 - val_loss: 0.0153 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - loss: 0.1358 - val_loss: 0.0426 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.1595 - val_loss: 0.0044 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.1054 - val_loss: 0.0555 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.1250 - val_loss: 0.0287 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.1014 - val_loss: 0.0107 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - loss: 0.1747 - val_loss: 0.0109 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.1780 - val_loss: 0.0569 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.0589 - val_loss: 0.0660 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - loss: 0.1236 - val_loss: 0.0150 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.1161 - val_loss: 0.0573 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 55ms/step - loss: 0.0848 - val_loss: 0.0269 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - loss: 0.0940 - val_loss: 0.0182 51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 15ms/step Sample raw predictions (after inverse transform and clipping): [0. 0. 0. 0. 0.08365785] RMSE = 0.62742025 Validation R-squared for item 2103: -0.3039228916168213 50/50 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=6.63416862487793 True y_val range (after inverse transform): min=0.0, max=6.0
-----------------------------------
Current item is 2475
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=10
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.5
x_eval_time shape before reshape: (1637, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1917, 14)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (174, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (176, 20)
Model: "sequential_82"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_165 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_70 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_166 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_71 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_81 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 59ms/step - loss: 9412.5967 - val_loss: 1445.9342 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 2485.5779 - val_loss: 34.6208 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - loss: 113.9763 - val_loss: 24.0422 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - loss: 71.7826 - val_loss: 13.7146 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 28.8629 - val_loss: 7.3721 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 16.3532 - val_loss: 2.0382 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 10.3375 - val_loss: 1.0634 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 7.5515 - val_loss: 0.3124 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 6.1591 - val_loss: 0.5202 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - loss: 4.5108 - val_loss: 0.3067 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 3.1653 - val_loss: 0.1350 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 2.3322 - val_loss: 0.0822 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - loss: 2.3853 - val_loss: 0.0724 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - loss: 2.0034 - val_loss: 0.0579 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - loss: 1.6176 - val_loss: 0.0412 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.8917 - val_loss: 0.0542 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.5413 - val_loss: 0.1785 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - loss: 8.3782 - val_loss: 0.0046 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - loss: 0.3461 - val_loss: 0.0104 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.1875 - val_loss: 0.0164 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.2140 - val_loss: 0.0033 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - loss: 0.1797 - val_loss: 0.0562 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.1451 - val_loss: 0.1814 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - loss: 0.6547 - val_loss: 0.0044 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.2284 - val_loss: 0.0046 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.8703 - val_loss: 16.1011 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 8.3775 - val_loss: 0.0050 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.4041 - val_loss: 0.0018 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.0930 - val_loss: 0.0016 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 56ms/step - loss: 0.2635 - val_loss: 0.0028 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.1184 - val_loss: 0.0016 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.1084 - val_loss: 0.0016 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.0506 - val_loss: 0.0015 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.0625 - val_loss: 0.0015 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.0665 - val_loss: 0.0016 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.0686 - val_loss: 0.0015 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.0427 - val_loss: 0.0015 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.0468 - val_loss: 0.0015 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.0946 - val_loss: 0.0015 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.0618 - val_loss: 0.0015 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.0334 - val_loss: 0.0020 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.1115 - val_loss: 0.0015 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.0175 - val_loss: 0.0015 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.0549 - val_loss: 0.0024 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.0258 - val_loss: 0.0015 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.0182 - val_loss: 0.0016 Epoch 47/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.0119 - val_loss: 0.0021 51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 15ms/step Sample raw predictions (after inverse transform and clipping): [0.21895541 0.19312853 0.2019254 0.07124845 0.07927851] RMSE = 0.5988919 Validation R-squared for item 2475: -0.02789163589477539 52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 12ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=0.3731420040130615 True y_val range (after inverse transform): min=0.0, max=5.0
-----------------------------------
Current item is 2359
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=6
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.8333333333333333
x_eval_time shape before reshape: (1662, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1942, 14)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (210, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (209, 20)
store_x_eval.shape: (184, 20)
Model: "sequential_83"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_167 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_72 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_168 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_73 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_82 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 59ms/step - loss: 9365.9268 - val_loss: 190.7198 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 494.0857 - val_loss: 9.5514 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 73.7295 - val_loss: 3.4019 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 25.3000 - val_loss: 1.3396 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 23.3465 - val_loss: 0.9357 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 14.4071 - val_loss: 0.9817 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 10.2439 - val_loss: 0.4970 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 7.8465 - val_loss: 0.3834 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 5.3499 - val_loss: 0.2635 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 4.8258 - val_loss: 0.1938 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 3.7411 - val_loss: 0.1728 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 3.5044 - val_loss: 0.1127 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 2.6467 - val_loss: 0.2827 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 2.2827 - val_loss: 0.0975 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 1.8433 - val_loss: 0.0667 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 1.8669 - val_loss: 0.2220 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 1.6133 - val_loss: 0.3116 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 1.2587 - val_loss: 0.0684 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 1.1905 - val_loss: 0.1103 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 1.0209 - val_loss: 0.0911 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 1.0325 - val_loss: 0.0453 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 0.7883 - val_loss: 0.0235 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.7712 - val_loss: 0.1041 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.8371 - val_loss: 0.1095 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.6186 - val_loss: 0.0644 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.4686 - val_loss: 0.1097 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 0.4329 - val_loss: 0.0107 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 0.3530 - val_loss: 0.0622 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 0.3395 - val_loss: 0.2616 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 57ms/step - loss: 0.2787 - val_loss: 0.0143 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 0.3031 - val_loss: 0.0430 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 0.3093 - val_loss: 0.2819 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 0.3056 - val_loss: 0.3881 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 0.2185 - val_loss: 0.2442 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 0.2504 - val_loss: 0.0281 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 0.1827 - val_loss: 0.0144 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 58ms/step - loss: 0.1767 - val_loss: 0.0138 52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 15ms/step Sample raw predictions (after inverse transform and clipping): [0.4330304 0.13572162 0.07607672 0.08268166 0.51625425] RMSE = 0.5034702 Validation R-squared for item 2359: -0.2726236581802368 52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=2.137045383453369 True y_val range (after inverse transform): min=0.0, max=5.0
-----------------------------------
Current item is 1428
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=11
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.7272727272727273
x_eval_time shape before reshape: (1660, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1940, 14)
store_x_eval.shape: (216, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (170, 20)
store_x_eval.shape: (198, 20)
Model: "sequential_84"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_169 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_74 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_170 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_75 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_83 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 61ms/step - loss: 38466.8789 - val_loss: 1101.1453 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 4987.2612 - val_loss: 172.3611 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 166.7888 - val_loss: 18.7164 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 55.3474 - val_loss: 2.4334 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 30.0404 - val_loss: 3.2688 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 19.2536 - val_loss: 0.5350 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 13.1905 - val_loss: 0.7582 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 9.1895 - val_loss: 1.1327 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 6.6861 - val_loss: 0.3294 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 4.8286 - val_loss: 0.1445 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 3.5145 - val_loss: 0.3638 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 2.9436 - val_loss: 0.0561 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 2.3528 - val_loss: 0.4365 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 1.9683 - val_loss: 0.0453 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 1.6058 - val_loss: 0.1601 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 1.2498 - val_loss: 0.1273 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 1.1708 - val_loss: 0.0190 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.8044 - val_loss: 0.3787 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 0.8620 - val_loss: 0.1376 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 0.6681 - val_loss: 0.0103 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 0.6015 - val_loss: 0.1685 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 0.5587 - val_loss: 0.0138 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 0.6405 - val_loss: 0.0086 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 0.4801 - val_loss: 0.2474 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.3793 - val_loss: 0.0061 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 0.3368 - val_loss: 0.0111 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 0.5011 - val_loss: 0.0058 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 0.3236 - val_loss: 0.3376 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 0.4763 - val_loss: 0.0726 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 0.3592 - val_loss: 0.0102 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 0.2727 - val_loss: 0.0611 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 0.3624 - val_loss: 0.0071 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.1940 - val_loss: 0.0762 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 0.3658 - val_loss: 0.0209 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.1960 - val_loss: 0.5810 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.8062 - val_loss: 0.3015 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 0.3248 - val_loss: 0.0356 52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 16ms/step Sample raw predictions (after inverse transform and clipping): [0. 0. 0.06808062 0. 0. ] RMSE = 0.97091347 Validation R-squared for item 1428: -0.3451046943664551 52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=4.895780086517334 True y_val range (after inverse transform): min=0.0, max=8.0
-----------------------------------
Current item is 2237
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=8
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.75
x_eval_time shape before reshape: (1609, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1889, 14)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (172, 20)
store_x_eval.shape: (169, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (203, 20)
Model: "sequential_85"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_171 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_76 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_172 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_77 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_84 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 61ms/step - loss: 35906.1133 - val_loss: 3098.5312 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 9692.7617 - val_loss: 552.1101 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 1690.1912 - val_loss: 175.4864 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 766.7796 - val_loss: 447.1161 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 1100.0265 - val_loss: 79.4871 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 407.0858 - val_loss: 47.7252 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 265.1601 - val_loss: 31.2024 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 184.3420 - val_loss: 22.5759 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 133.9280 - val_loss: 17.4127 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 102.7733 - val_loss: 15.1096 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 80.3628 - val_loss: 10.9220 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 66.7212 - val_loss: 9.1333 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 54.1863 - val_loss: 6.7583 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 40.6570 - val_loss: 5.4371 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 32.6830 - val_loss: 4.8515 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 27.5125 - val_loss: 3.7809 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 59ms/step - loss: 22.3841 - val_loss: 3.4011 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 19.6381 - val_loss: 3.1840 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 16.0307 - val_loss: 2.7746 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 13.7825 - val_loss: 2.4479 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 11.9829 - val_loss: 2.2525 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 10.3095 - val_loss: 2.0521 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 9.2209 - val_loss: 1.9278 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 8.3043 - val_loss: 1.6993 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 7.3547 - val_loss: 1.6626 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 6.6985 - val_loss: 1.8397 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 5.3176 - val_loss: 1.4785 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 5.9919 - val_loss: 1.2876 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 4.6347 - val_loss: 1.1037 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 4.1668 - val_loss: 0.9896 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 4.0469 - val_loss: 0.9301 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 3.4501 - val_loss: 0.6807 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 2.8242 - val_loss: 0.6639 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 2.7743 - val_loss: 0.5368 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 2.3835 - val_loss: 0.4270 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 2.0215 - val_loss: 0.3550 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 1.8976 - val_loss: 0.2845 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 1.7322 - val_loss: 0.3456 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 1.6148 - val_loss: 0.2627 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 1.4451 - val_loss: 0.2187 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 1.1542 - val_loss: 0.1632 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 1.1060 - val_loss: 0.2591 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 1.1326 - val_loss: 0.1438 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.8585 - val_loss: 0.1240 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.7892 - val_loss: 0.1355 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.8256 - val_loss: 0.1008 Epoch 47/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.6729 - val_loss: 0.1728 Epoch 48/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.6883 - val_loss: 0.0846 Epoch 49/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.6339 - val_loss: 0.0933 Epoch 50/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.4695 - val_loss: 0.0824 Epoch 51/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.5237 - val_loss: 0.1078 Epoch 52/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 61ms/step - loss: 0.3803 - val_loss: 0.0703 Epoch 53/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.4690 - val_loss: 0.0620 Epoch 54/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 61ms/step - loss: 0.3413 - val_loss: 0.0518 Epoch 55/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.3548 - val_loss: 0.2102 Epoch 56/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.3189 - val_loss: 0.1197 Epoch 57/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.3782 - val_loss: 0.0510 Epoch 58/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.2319 - val_loss: 0.0927 Epoch 59/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.2630 - val_loss: 0.0687 Epoch 60/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 61ms/step - loss: 0.2632 - val_loss: 0.0904 Epoch 61/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.2405 - val_loss: 0.0333 Epoch 62/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.2046 - val_loss: 0.0379 Epoch 63/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.1729 - val_loss: 0.1338 Epoch 64/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.2571 - val_loss: 0.0399 Epoch 65/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.1710 - val_loss: 0.0373 Epoch 66/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.3139 - val_loss: 0.0308 Epoch 67/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 61ms/step - loss: 0.1369 - val_loss: 0.0311 Epoch 68/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 61ms/step - loss: 0.1401 - val_loss: 0.0990 Epoch 69/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.5754 - val_loss: 0.1784 Epoch 70/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 61ms/step - loss: 0.5505 - val_loss: 0.1179 Epoch 71/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.2199 - val_loss: 0.0234 Epoch 72/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 61ms/step - loss: 0.1643 - val_loss: 0.3613 Epoch 73/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 61ms/step - loss: 0.3534 - val_loss: 0.0332 Epoch 74/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.1499 - val_loss: 0.0130 Epoch 75/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 61ms/step - loss: 0.2689 - val_loss: 0.0211 Epoch 76/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 61ms/step - loss: 0.2733 - val_loss: 0.3388 Epoch 77/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.4954 - val_loss: 0.2240 Epoch 78/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.3152 - val_loss: 0.1716 Epoch 79/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 61ms/step - loss: 0.3177 - val_loss: 0.0240 Epoch 80/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 61ms/step - loss: 0.4918 - val_loss: 0.2408 Epoch 81/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.4156 - val_loss: 0.0266 Epoch 82/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.2693 - val_loss: 0.0113 Epoch 83/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.0843 - val_loss: 0.2548 Epoch 84/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.1940 - val_loss: 0.0148 Epoch 85/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 61ms/step - loss: 0.1293 - val_loss: 0.3965 Epoch 86/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.2155 - val_loss: 0.3089 Epoch 87/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.8342 - val_loss: 0.0415 Epoch 88/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.1534 - val_loss: 0.4001 Epoch 89/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.2687 - val_loss: 0.0178 Epoch 90/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.1409 - val_loss: 0.3309 Epoch 91/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 61ms/step - loss: 0.2876 - val_loss: 0.0309 Epoch 92/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6s 60ms/step - loss: 0.1821 - val_loss: 0.3889 52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 17ms/step Sample raw predictions (after inverse transform and clipping): [0.35188085 0.0742069 0.04478479 0.27248406 0.169245 ] RMSE = 0.8047374 Validation R-squared for item 2237: -0.7029622793197632 51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=10.792951583862305 True y_val range (after inverse transform): min=0.0, max=6.0
-----------------------------------
Current item is 1016
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=22
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.7272727272727273
x_eval_time shape before reshape: (1573, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1853, 14)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (164, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (161, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (205, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (181, 20)
Model: "sequential_86"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_173 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_78 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_174 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_79 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_85 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 63ms/step - loss: 8923.8477 - val_loss: 446.0733 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 585.8268 - val_loss: 41.1710 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 75.2053 - val_loss: 2.0860 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 5.8188 - val_loss: 0.0454 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 10.3395 - val_loss: 0.0849 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 1.9538 - val_loss: 0.0342 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 1.4927 - val_loss: 0.0214 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 1.1630 - val_loss: 0.1652 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.9384 - val_loss: 0.1279 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.9271 - val_loss: 0.0940 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.6017 - val_loss: 0.0327 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.6964 - val_loss: 0.0914 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.7704 - val_loss: 0.0204 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.3228 - val_loss: 0.0211 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.5552 - val_loss: 0.0409 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.4082 - val_loss: 0.0210 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.4093 - val_loss: 0.3394 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.2546 - val_loss: 0.0199 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.2221 - val_loss: 0.0410 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.1545 - val_loss: 0.0205 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 0.3061 - val_loss: 0.0684 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 63ms/step - loss: 0.2440 - val_loss: 1.0419 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.4100 - val_loss: 0.0257 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0456 - val_loss: 0.0936 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 63ms/step - loss: 0.0454 - val_loss: 0.0148 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0545 - val_loss: 0.0598 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.1556 - val_loss: 0.0149 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0604 - val_loss: 0.0213 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0713 - val_loss: 0.1428 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0981 - val_loss: 0.0273 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.1685 - val_loss: 0.1215 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0597 - val_loss: 0.0130 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0649 - val_loss: 0.0106 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0711 - val_loss: 0.0099 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0408 - val_loss: 0.0269 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0344 - val_loss: 0.0331 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.1083 - val_loss: 0.0118 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.1911 - val_loss: 0.0137 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.1547 - val_loss: 0.0140 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0544 - val_loss: 0.0161 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.1445 - val_loss: 0.0099 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.3114 - val_loss: 0.0095 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0554 - val_loss: 0.0253 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.2086 - val_loss: 0.0214 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 63ms/step - loss: 0.1929 - val_loss: 0.0183 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0879 - val_loss: 0.0641 Epoch 47/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.3170 - val_loss: 0.0097 Epoch 48/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0879 - val_loss: 0.0299 Epoch 49/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0576 - val_loss: 0.0260 Epoch 50/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0240 - val_loss: 0.0102 Epoch 51/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0718 - val_loss: 0.0101 Epoch 52/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.2780 - val_loss: 0.0094 Epoch 53/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0189 - val_loss: 0.0092 Epoch 54/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0185 - val_loss: 0.0101 Epoch 55/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0286 - val_loss: 0.0102 Epoch 56/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0123 - val_loss: 0.0097 Epoch 57/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0196 - val_loss: 0.0102 Epoch 58/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0714 - val_loss: 0.8117 Epoch 59/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 1.1675 - val_loss: 0.0112 Epoch 60/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0785 - val_loss: 0.0106 Epoch 61/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0140 - val_loss: 0.0101 Epoch 62/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.0377 - val_loss: 0.0098 Epoch 63/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 63ms/step - loss: 0.0159 - val_loss: 0.0096 52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 17ms/step Sample raw predictions (after inverse transform and clipping): [2.4353821 2.4648445 2.0017042 1.867104 0.9972872] RMSE = 2.1586742 Validation R-squared for item 1016: -0.09603071212768555 50/50 ━━━━━━━━━━━━━━━━━━━━ 1s 15ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=6.3863701820373535 True y_val range (after inverse transform): min=0.0, max=16.0
-----------------------------------
Current item is 2114
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=10
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.6000000000000001
x_eval_time shape before reshape: (1659, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1939, 14)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (205, 20)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (175, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (176, 20)
Model: "sequential_87"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_175 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_80 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_176 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_81 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_86 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 66ms/step - loss: 34148.1094 - val_loss: 5815.8286 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 4641.6841 - val_loss: 77.1193 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 63ms/step - loss: 160.8722 - val_loss: 44.5445 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 63ms/step - loss: 45.6440 - val_loss: 23.8772 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 63ms/step - loss: 24.9034 - val_loss: 6.7086 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 63ms/step - loss: 18.1216 - val_loss: 3.2390 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 11.7563 - val_loss: 1.0781 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 63ms/step - loss: 9.5555 - val_loss: 1.2092 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 63ms/step - loss: 8.1537 - val_loss: 1.2637 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 63ms/step - loss: 6.9849 - val_loss: 0.3593 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 63ms/step - loss: 5.9574 - val_loss: 2.4656 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 63ms/step - loss: 5.4623 - val_loss: 0.3310 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 4.2194 - val_loss: 1.7716 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 3.9904 - val_loss: 0.3303 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 2.8858 - val_loss: 0.1154 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 2.3539 - val_loss: 0.1850 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 2.2482 - val_loss: 0.2820 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 1.7259 - val_loss: 0.4737 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 1.6392 - val_loss: 0.0176 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 1.4088 - val_loss: 0.0515 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 1.2060 - val_loss: 0.0236 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 1.3442 - val_loss: 0.0751 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 1.2429 - val_loss: 0.3209 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 1.0368 - val_loss: 0.1124 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.9989 - val_loss: 0.0740 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.7074 - val_loss: 0.1335 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.6768 - val_loss: 0.7925 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 63ms/step - loss: 0.6492 - val_loss: 0.0512 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 62ms/step - loss: 0.6420 - val_loss: 0.1251 52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 17ms/step Sample raw predictions (after inverse transform and clipping): [0. 0. 0. 0. 0.] RMSE = 0.6571891 Validation R-squared for item 2114: -0.9010403156280518 52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 14ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=7.650656700134277 True y_val range (after inverse transform): min=0.0, max=6.0
-----------------------------------
Current item is 2236
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=5
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.8
x_eval_time shape before reshape: (1626, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1906, 14)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (167, 20)
store_x_eval.shape: (178, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (202, 20)
Model: "sequential_88"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_177 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_82 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_178 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_83 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_87 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 66ms/step - loss: 60931.7383 - val_loss: 18196.5117 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 54921.3125 - val_loss: 703.8808 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 2274.4390 - val_loss: 69.0283 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 551.3114 - val_loss: 27.0784 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 261.7172 - val_loss: 14.9578 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 147.7003 - val_loss: 3.5253 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 189.1150 - val_loss: 8.1673 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 248.2841 - val_loss: 13.5123 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 149.0077 - val_loss: 15.1317 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 83.7115 - val_loss: 6.9724 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 60.3978 - val_loss: 4.7333 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 49.8008 - val_loss: 5.4633 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 38.1581 - val_loss: 3.4983 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 35.3562 - val_loss: 8.0386 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 32.1800 - val_loss: 4.1421 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 28.0170 - val_loss: 2.5485 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 21.0005 - val_loss: 7.2916 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 20.1295 - val_loss: 2.2763 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 18.7297 - val_loss: 3.6458 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 15.1441 - val_loss: 7.6980 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 14.9756 - val_loss: 0.9691 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 11.4808 - val_loss: 0.7378 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 10.3961 - val_loss: 4.0869 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 10.9336 - val_loss: 2.5544 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 8.9745 - val_loss: 1.7377 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 7.3794 - val_loss: 6.7601 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 8.3014 - val_loss: 0.5705 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 6.2151 - val_loss: 0.5179 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 6.6011 - val_loss: 0.4941 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 6.6561 - val_loss: 2.3238 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 6.2074 - val_loss: 0.8638 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 6.9182 - val_loss: 2.3331 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 12.7313 - val_loss: 2.2567 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 11.0322 - val_loss: 0.7912 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 7.2435 - val_loss: 2.1257 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 6.4761 - val_loss: 0.9910 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 5.7495 - val_loss: 0.3297 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 4.9290 - val_loss: 0.3290 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 4.1679 - val_loss: 0.2693 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 3.9212 - val_loss: 2.8054 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 3.5207 - val_loss: 0.2506 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 3.5214 - val_loss: 2.3607 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 2.9373 - val_loss: 0.2157 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 2.4879 - val_loss: 0.1856 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 2.5557 - val_loss: 0.1589 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 2.3990 - val_loss: 0.2718 Epoch 47/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 2.4032 - val_loss: 0.7873 Epoch 48/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 2.2215 - val_loss: 0.6562 Epoch 49/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 2.0798 - val_loss: 0.5002 Epoch 50/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 1.7341 - val_loss: 0.3142 Epoch 51/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 1.7402 - val_loss: 0.3478 Epoch 52/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 1.7864 - val_loss: 0.5294 Epoch 53/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 1.9512 - val_loss: 0.1546 Epoch 54/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 1.7519 - val_loss: 0.0617 Epoch 55/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 1.3610 - val_loss: 3.0020 Epoch 56/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 2.0613 - val_loss: 0.1477 Epoch 57/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 1.0915 - val_loss: 2.1934 Epoch 58/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 1.8796 - val_loss: 0.3350 Epoch 59/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 1.4961 - val_loss: 0.2938 Epoch 60/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 1.5036 - val_loss: 0.0486 Epoch 61/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 1.2643 - val_loss: 0.0517 Epoch 62/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 64ms/step - loss: 1.1046 - val_loss: 0.1592 Epoch 63/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.9828 - val_loss: 0.8942 Epoch 64/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 1.0303 - val_loss: 0.1532 Epoch 65/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 1.1882 - val_loss: 0.1869 Epoch 66/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.9287 - val_loss: 0.3617 Epoch 67/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.7628 - val_loss: 0.7922 Epoch 68/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.8046 - val_loss: 0.0245 Epoch 69/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.6668 - val_loss: 0.0250 Epoch 70/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.5698 - val_loss: 0.2881 Epoch 71/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.8082 - val_loss: 0.2803 Epoch 72/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.5431 - val_loss: 0.6373 Epoch 73/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.7606 - val_loss: 2.3125 Epoch 74/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.8540 - val_loss: 0.1715 Epoch 75/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 1.4041 - val_loss: 0.0158 Epoch 76/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.8022 - val_loss: 0.0193 Epoch 77/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.6962 - val_loss: 0.4107 Epoch 78/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.6775 - val_loss: 0.1024 Epoch 79/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.6764 - val_loss: 0.5605 Epoch 80/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.9005 - val_loss: 0.2788 Epoch 81/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.3058 - val_loss: 0.0120 Epoch 82/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.2849 - val_loss: 2.4434 Epoch 83/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.6922 - val_loss: 0.1395 Epoch 84/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 1.6178 - val_loss: 0.0793 Epoch 85/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.2732 - val_loss: 0.2161 Epoch 86/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.3751 - val_loss: 0.0786 Epoch 87/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.2321 - val_loss: 0.0579 Epoch 88/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.2737 - val_loss: 0.0227 Epoch 89/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.1167 - val_loss: 0.3834 Epoch 90/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 65ms/step - loss: 0.3210 - val_loss: 0.1243 Epoch 91/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.5968 - val_loss: 0.5817 51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step Sample raw predictions (after inverse transform and clipping): [0.19208461 0.37189746 0.47617587 0.3737444 0.30523837] RMSE = 0.52220756 Validation R-squared for item 2236: -0.2848362922668457 51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 15ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=0.9125242233276367 True y_val range (after inverse transform): min=0.0, max=4.0
-----------------------------------
Current item is 1172
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=30
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.7666666666666666
x_eval_time shape before reshape: (1627, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1907, 14)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (216, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (172, 20)
store_x_eval.shape: (184, 20)
Model: "sequential_89"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_179 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_84 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_180 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_85 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_88 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 67ms/step - loss: 28333.6367 - val_loss: 1906.9412 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 2945.6123 - val_loss: 20.3263 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 146.3445 - val_loss: 5.5245 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 54.6891 - val_loss: 2.7626 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 27.4813 - val_loss: 3.5591 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 17.9773 - val_loss: 0.3740 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 12.3851 - val_loss: 1.6670 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 9.3086 - val_loss: 9.4532 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 95.3638 - val_loss: 1.4107 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 59.0693 - val_loss: 0.5416 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 26.8820 - val_loss: 1.9346 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 17.2062 - val_loss: 0.3527 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 10.3359 - val_loss: 0.4483 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 8.6844 - val_loss: 0.6080 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 6.0948 - val_loss: 0.9279 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 5.5852 - val_loss: 2.1914 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 4.6008 - val_loss: 0.3030 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 3.9576 - val_loss: 0.3910 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 4.2066 - val_loss: 0.1964 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 2.4054 - val_loss: 0.1915 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 2.5420 - val_loss: 0.3455 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 2.1845 - val_loss: 0.5793 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 1.9163 - val_loss: 2.6166 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 1.9376 - val_loss: 0.1257 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 1.9324 - val_loss: 0.1128 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 1.7069 - val_loss: 0.0205 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 1.2848 - val_loss: 1.6870 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 1.6824 - val_loss: 0.0569 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.8168 - val_loss: 0.0331 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.9169 - val_loss: 0.0144 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.7331 - val_loss: 0.2180 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.7197 - val_loss: 3.8089 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.8007 - val_loss: 0.0406 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.5251 - val_loss: 0.6182 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.6502 - val_loss: 0.0078 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.3353 - val_loss: 0.5958 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.3470 - val_loss: 0.4124 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 5.9941 - val_loss: 0.0731 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 1.1937 - val_loss: 0.0029 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.5281 - val_loss: 0.0111 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.3368 - val_loss: 0.0018 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 1.0972 - val_loss: 0.7724 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 3.3587 - val_loss: 0.0584 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 1.3964 - val_loss: 0.0080 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.7911 - val_loss: 0.0120 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.3834 - val_loss: 0.0052 Epoch 47/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.4767 - val_loss: 0.0197 Epoch 48/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.4160 - val_loss: 0.0031 Epoch 49/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.2573 - val_loss: 0.0342 Epoch 50/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.2889 - val_loss: 0.0079 Epoch 51/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.1585 - val_loss: 0.0046 51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step Sample raw predictions (after inverse transform and clipping): [0.48300707 0. 0.35337994 0.17798683 0.64058304] RMSE = 1.4293249 Validation R-squared for item 1172: -0.2067563533782959 51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 16ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=14.141376495361328 True y_val range (after inverse transform): min=0.0, max=23.0
-----------------------------------
Current item is 2443
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=8
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.25
x_eval_time shape before reshape: (1657, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1937, 14)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (223, 20)
store_x_eval.shape: (212, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (174, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (206, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (190, 20)
Model: "sequential_90"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_181 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_86 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_182 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_87 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_89 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 69ms/step - loss: 8864.8467 - val_loss: 278.3709 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 636.4620 - val_loss: 42.4217 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 148.2120 - val_loss: 7.5348 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 47.5936 - val_loss: 6.1026 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 33.8543 - val_loss: 2.5773 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 16.4389 - val_loss: 1.7642 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 17.4370 - val_loss: 3.6098 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 11.6453 - val_loss: 0.5502 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 2.2272 - val_loss: 0.1828 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 2.2771 - val_loss: 0.3212 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 2.8712 - val_loss: 0.1371 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 2.1052 - val_loss: 3.9842 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 3.2921 - val_loss: 0.1209 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 2.1808 - val_loss: 0.0278 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 1.2795 - val_loss: 0.0330 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.9899 - val_loss: 1.3131 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.8943 - val_loss: 0.0171 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.4222 - val_loss: 0.0055 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.3730 - val_loss: 0.0131 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.1982 - val_loss: 0.0765 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.1458 - val_loss: 0.0087 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 2.0024 - val_loss: 0.0303 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.1923 - val_loss: 0.0115 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.2658 - val_loss: 0.0121 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.3012 - val_loss: 0.4320 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 1.6817 - val_loss: 0.0127 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 66ms/step - loss: 0.2560 - val_loss: 0.0242 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.0771 - val_loss: 0.0050 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.1192 - val_loss: 0.0051 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.1292 - val_loss: 0.0052 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.0815 - val_loss: 0.0049 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.1348 - val_loss: 0.0045 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.0847 - val_loss: 0.0402 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.0641 - val_loss: 0.0065 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.0844 - val_loss: 0.0054 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.1272 - val_loss: 0.0075 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.0509 - val_loss: 0.0043 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.0774 - val_loss: 0.3006 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.1052 - val_loss: 0.0382 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.0823 - val_loss: 0.0060 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.0473 - val_loss: 0.0290 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.1634 - val_loss: 0.0050 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.0846 - val_loss: 0.0092 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.1101 - val_loss: 0.0137 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.0412 - val_loss: 0.0178 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.1236 - val_loss: 0.0107 Epoch 47/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 67ms/step - loss: 0.0313 - val_loss: 0.0671 52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step Sample raw predictions (after inverse transform and clipping): [0.30323613 0.46130294 0.4934683 0.3977915 0.35509613] RMSE = 0.7468081 Validation R-squared for item 2443: -0.05250751972198486 52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 15ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=1.19840669631958 True y_val range (after inverse transform): min=0.0, max=10.0
-----------------------------------
Current item is 1004
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=36
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.7222222222222222
x_eval_time shape before reshape: (1680, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1960, 14)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (162, 20)
store_x_eval.shape: (205, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (213, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (208, 20)
Model: "sequential_91"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_183 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_88 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_184 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_89 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_90 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 71ms/step - loss: 3144.2327 - val_loss: 33.5587 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 82.9533 - val_loss: 5.7705 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 22.6164 - val_loss: 2.0536 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 8.5708 - val_loss: 0.7341 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 4.7551 - val_loss: 0.9842 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 3.6357 - val_loss: 0.2936 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 2.1022 - val_loss: 0.2480 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 1.5662 - val_loss: 0.5586 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 1.0424 - val_loss: 0.0927 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.7866 - val_loss: 1.0064 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.7424 - val_loss: 0.0536 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.5426 - val_loss: 0.1050 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.4111 - val_loss: 0.0615 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.3940 - val_loss: 0.0203 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.2663 - val_loss: 0.0186 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.5111 - val_loss: 0.0221 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.1511 - val_loss: 0.0337 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.1710 - val_loss: 0.0821 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.2000 - val_loss: 0.0436 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.1767 - val_loss: 0.0554 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.1028 - val_loss: 0.1914 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.0864 - val_loss: 0.0320 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.1488 - val_loss: 0.0062 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.1510 - val_loss: 0.0055 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.0808 - val_loss: 0.0124 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.2283 - val_loss: 0.0063 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.0791 - val_loss: 0.0113 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.0820 - val_loss: 0.0389 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.0673 - val_loss: 0.0104 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 70ms/step - loss: 0.0887 - val_loss: 0.0917 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.1284 - val_loss: 0.0056 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.0390 - val_loss: 0.0421 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.0436 - val_loss: 0.0149 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 68ms/step - loss: 0.0349 - val_loss: 0.0290 50/50 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step Sample raw predictions (after inverse transform and clipping): [0.66863996 0.04824355 1.6085396 4.9664354 0.7180111 ] RMSE = 3.1583416 Validation R-squared for item 1004: -0.4444699287414551 53/53 ━━━━━━━━━━━━━━━━━━━━ 1s 16ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=8.026754379272461 True y_val range (after inverse transform): min=0.0, max=23.0
-----------------------------------
Current item is 2297
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=8
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.625
x_eval_time shape before reshape: (1694, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1974, 14)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (213, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (220, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (199, 20)
Model: "sequential_92"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_185 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_90 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_186 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_91 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_91 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 70ms/step - loss: 20200.9199 - val_loss: 2367.4358 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 3315.8997 - val_loss: 40.7881 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 590.1129 - val_loss: 16.0045 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 220.4137 - val_loss: 8.5887 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 112.3401 - val_loss: 1.4584 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 59.8091 - val_loss: 4.6452 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 39.8194 - val_loss: 0.3325 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 25.9247 - val_loss: 0.4017 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 16.2369 - val_loss: 0.1149 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 13.7494 - val_loss: 4.0648 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 7.5500 - val_loss: 0.1244 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 5.0763 - val_loss: 2.2661 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 4.1615 - val_loss: 0.4402 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 3.1275 - val_loss: 0.1856 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 2.7987 - val_loss: 0.0710 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 2.2142 - val_loss: 0.6639 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 1.6944 - val_loss: 0.4843 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 1.8092 - val_loss: 1.1986 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 2.3310 - val_loss: 0.5549 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 2.2343 - val_loss: 0.1168 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 1.5023 - val_loss: 0.0206 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 1.3591 - val_loss: 1.4173 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 1.4182 - val_loss: 0.0239 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 1.5187 - val_loss: 0.0398 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.9765 - val_loss: 0.0293 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.9338 - val_loss: 1.0571 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 1.1464 - val_loss: 0.2968 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 1.6975 - val_loss: 0.0567 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.8730 - val_loss: 2.6570 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 1.4159 - val_loss: 0.4466 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.5926 - val_loss: 0.0691 49/49 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step Sample raw predictions (after inverse transform and clipping): [0.13341664 0.42512035 0.92939734 0.46399993 0. ] RMSE = 0.7633648 Validation R-squared for item 2297: -0.2760545015335083 53/53 ━━━━━━━━━━━━━━━━━━━━ 1s 16ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=1.9584254026412964 True y_val range (after inverse transform): min=0.0, max=5.0
-----------------------------------
Current item is 1347
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=18
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.611111111111111
x_eval_time shape before reshape: (1690, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1970, 14)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (205, 20)
store_x_eval.shape: (223, 20)
store_x_eval.shape: (174, 20)
store_x_eval.shape: (176, 20)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (220, 20)
Model: "sequential_93"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_187 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_92 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_188 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_93 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_92 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 72ms/step - loss: 18057.6504 - val_loss: 2621.0117 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 1595.1162 - val_loss: 31.2051 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 74.5870 - val_loss: 6.8534 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 21.3470 - val_loss: 2.5522 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 9.9546 - val_loss: 1.1069 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 7.5987 - val_loss: 0.4591 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 6.4161 - val_loss: 0.5092 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 2.6037 - val_loss: 0.1205 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.7250 - val_loss: 0.0569 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.4639 - val_loss: 0.0170 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.2334 - val_loss: 0.0588 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.2327 - val_loss: 0.0282 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.1847 - val_loss: 0.0070 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.0983 - val_loss: 0.0051 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.1256 - val_loss: 0.0101 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.0799 - val_loss: 0.0058 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.2580 - val_loss: 0.0674 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.1226 - val_loss: 0.0706 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.0950 - val_loss: 0.0037 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.1553 - val_loss: 0.0133 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 70ms/step - loss: 0.0826 - val_loss: 0.0036 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.1042 - val_loss: 0.0528 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.1396 - val_loss: 0.1476 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.1052 - val_loss: 0.0048 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.0316 - val_loss: 0.0089 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.1060 - val_loss: 0.0084 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.0801 - val_loss: 0.0427 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.0989 - val_loss: 0.0033 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 70ms/step - loss: 0.0497 - val_loss: 0.0047 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.0591 - val_loss: 0.0142 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.0362 - val_loss: 0.0385 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.0968 - val_loss: 0.0138 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.0405 - val_loss: 0.0041 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.0659 - val_loss: 0.0081 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 70ms/step - loss: 0.2455 - val_loss: 0.0044 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.0284 - val_loss: 0.0079 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 69ms/step - loss: 0.0323 - val_loss: 0.0068 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 70ms/step - loss: 8.1734 - val_loss: 0.0225 50/50 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step Sample raw predictions (after inverse transform and clipping): [0.7583719 0.60148513 0.64859056 0.42187887 1.1037309 ] RMSE = 1.9945282 Validation R-squared for item 1347: -0.27146196365356445 53/53 ━━━━━━━━━━━━━━━━━━━━ 1s 16ms/step Predicted y_val range (after inverse transform and clipping): min=0.029141077771782875, max=2.4545814990997314 True y_val range (after inverse transform): min=0.0, max=11.0
-----------------------------------
Current item is 2507
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=10
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.0
x_eval_time shape before reshape: (1611, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1891, 14)
store_x_eval.shape: (180, 20)
store_x_eval.shape: (178, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (205, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (169, 20)
store_x_eval.shape: (205, 20)
store_x_eval.shape: (199, 20)
Model: "sequential_94"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_189 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_94 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_190 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_95 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_93 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 74ms/step - loss: 5045.1255 - val_loss: 14.3661 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 48.3462 - val_loss: 6.0955 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 11.8565 - val_loss: 3.5488 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 5.1381 - val_loss: 1.7383 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 3.1490 - val_loss: 0.6675 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 2.0620 - val_loss: 0.3997 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 1.5252 - val_loss: 0.3390 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 1.0590 - val_loss: 0.1710 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.9370 - val_loss: 0.1428 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.6628 - val_loss: 0.0814 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.6023 - val_loss: 0.2840 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 71ms/step - loss: 0.4327 - val_loss: 0.0848 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.5873 - val_loss: 0.1352 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.7596 - val_loss: 0.0600 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.6082 - val_loss: 0.0937 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.4349 - val_loss: 0.0431 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.3078 - val_loss: 0.0396 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.2941 - val_loss: 0.0370 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.1955 - val_loss: 0.7451 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.2799 - val_loss: 0.0965 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.2239 - val_loss: 0.0182 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.1574 - val_loss: 0.0143 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.1303 - val_loss: 0.1382 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.2218 - val_loss: 0.0307 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.1162 - val_loss: 0.0127 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.2133 - val_loss: 0.0423 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.1700 - val_loss: 0.0118 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.1624 - val_loss: 0.0238 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.0908 - val_loss: 0.0330 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.0812 - val_loss: 0.0478 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.2059 - val_loss: 0.1485 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.0758 - val_loss: 0.0254 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.0974 - val_loss: 0.2549 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.1789 - val_loss: 0.0420 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.0380 - val_loss: 0.0258 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.0619 - val_loss: 0.0926 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 7s 71ms/step - loss: 0.0377 - val_loss: 0.0638 52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 19ms/step Sample raw predictions (after inverse transform and clipping): [1.5021943 1.2365146 1.422947 1.4174192 0.38766748] RMSE = 1.1200094 Validation R-squared for item 2507: -0.47574126720428467 51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 16ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=9.151969909667969 True y_val range (after inverse transform): min=0.0, max=10.0
-----------------------------------
Current item is 1505
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=30
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.43333333333333335
x_eval_time shape before reshape: (1558, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1838, 14)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (166, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (173, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (173, 20)
Model: "sequential_95"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_191 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_96 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_192 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_97 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_94 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 9s 76ms/step - loss: 40563.8398 - val_loss: 8338.0137 Epoch 2/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 25712.9648 - val_loss: 878.3768 Epoch 3/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 1816.9257 - val_loss: 103.9630 Epoch 4/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 488.8260 - val_loss: 69.7238 Epoch 5/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 244.5220 - val_loss: 41.8512 Epoch 6/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 148.7149 - val_loss: 48.3145 Epoch 7/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 88.2482 - val_loss: 30.5061 Epoch 8/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 57.4573 - val_loss: 26.2248 Epoch 9/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 33.6125 - val_loss: 39.7281 Epoch 10/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 24.1754 - val_loss: 8.1977 Epoch 11/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 15.9730 - val_loss: 4.0010 Epoch 12/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 11.8706 - val_loss: 2.6758 Epoch 13/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 10.5190 - val_loss: 1.6969 Epoch 14/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 8.7530 - val_loss: 1.4315 Epoch 15/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 4.8835 - val_loss: 1.9010 Epoch 16/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 4.4317 - val_loss: 0.5931 Epoch 17/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 3.5682 - val_loss: 0.7071 Epoch 18/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 2.2628 - val_loss: 1.6902 Epoch 19/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 2.5115 - val_loss: 0.2347 Epoch 20/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 2.6315 - val_loss: 0.2980 Epoch 21/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 1.8519 - val_loss: 0.1893 Epoch 22/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 1.8228 - val_loss: 0.2928 Epoch 23/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 1.2240 - val_loss: 1.1044 Epoch 24/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 1.4955 - val_loss: 0.8322 Epoch 25/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 1.0327 - val_loss: 0.3763 Epoch 26/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 1.5035 - val_loss: 0.7492 Epoch 27/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 71ms/step - loss: 0.9371 - val_loss: 0.1841 Epoch 28/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.7479 - val_loss: 0.8157 Epoch 29/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.9058 - val_loss: 0.1164 Epoch 30/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.5682 - val_loss: 0.0855 Epoch 31/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.7420 - val_loss: 0.6139 Epoch 32/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.7602 - val_loss: 0.1204 Epoch 33/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.9145 - val_loss: 0.2997 Epoch 34/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.6201 - val_loss: 0.3324 Epoch 35/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.6609 - val_loss: 0.0393 Epoch 36/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.4978 - val_loss: 0.0764 Epoch 37/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.4933 - val_loss: 0.0991 Epoch 38/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.7066 - val_loss: 0.2675 Epoch 39/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.4802 - val_loss: 0.0114 Epoch 40/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.6731 - val_loss: 0.0578 Epoch 41/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.4568 - val_loss: 0.0417 Epoch 42/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.2156 - val_loss: 0.0888 Epoch 43/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.4979 - val_loss: 0.0352 Epoch 44/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.3809 - val_loss: 0.1698 Epoch 45/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.6103 - val_loss: 0.0890 Epoch 46/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.3648 - val_loss: 0.0226 Epoch 47/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 1.8748 - val_loss: 0.0444 Epoch 48/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.1101 - val_loss: 0.0486 Epoch 49/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.1617 - val_loss: 0.0361 51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 19ms/step Sample raw predictions (after inverse transform and clipping): [0.00346838 0. 0. 0.06643267 0. ] RMSE = 4.012579 Validation R-squared for item 1505: -2.548703193664551 49/49 ━━━━━━━━━━━━━━━━━━━━ 1s 16ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=108.49539184570312 True y_val range (after inverse transform): min=0.0, max=13.0
-----------------------------------
Current item is 1460
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=18
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.7222222222222222
x_eval_time shape before reshape: (1594, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1874, 14)
store_x_eval.shape: (175, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (206, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (190, 20)
Model: "sequential_96"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_193 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_98 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_194 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_99 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_95 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 74ms/step - loss: 11100.2441 - val_loss: 428.4152 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 638.3618 - val_loss: 97.8230 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 287.7589 - val_loss: 78.9735 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 187.6889 - val_loss: 18.8228 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 88.0253 - val_loss: 7.6022 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 33.7375 - val_loss: 3.5570 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 21.9986 - val_loss: 1.9975 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 15.2219 - val_loss: 1.5466 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 19.1399 - val_loss: 2.1772 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 17.2427 - val_loss: 1.1211 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 5.1295 - val_loss: 0.3648 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 1.3704 - val_loss: 0.3209 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 1.7105 - val_loss: 0.0259 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.2627 - val_loss: 0.0136 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.3877 - val_loss: 0.0518 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 73ms/step - loss: 0.2283 - val_loss: 0.6485 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.3794 - val_loss: 0.0124 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.3344 - val_loss: 0.2719 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.1388 - val_loss: 0.0412 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.1275 - val_loss: 0.3158 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.3066 - val_loss: 0.0159 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 73ms/step - loss: 0.2559 - val_loss: 0.1270 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.1239 - val_loss: 0.0246 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.4407 - val_loss: 0.0384 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 72ms/step - loss: 0.0708 - val_loss: 0.0534 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 73ms/step - loss: 0.2069 - val_loss: 0.1669 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.3986 - val_loss: 0.0417 53/53 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step Sample raw predictions (after inverse transform and clipping): [0. 3.9262433 0.9943013 1.2408843 2.1202502] RMSE = 1.7123482 Validation R-squared for item 1460: -0.7177166938781738 50/50 ━━━━━━━━━━━━━━━━━━━━ 1s 17ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=7.560532093048096 True y_val range (after inverse transform): min=0.0, max=13.0
-----------------------------------
Current item is 2212
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=22
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.8181818181818182
x_eval_time shape before reshape: (1653, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1933, 14)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (217, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (208, 20)
store_x_eval.shape: (207, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (173, 20)
Model: "sequential_97"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_195 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_100 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_196 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_101 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_96 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 76ms/step - loss: 127880.2969 - val_loss: 52447.6719 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 145171.3750 - val_loss: 181.2726 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 3006.2612 - val_loss: 98.7306 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 1033.6169 - val_loss: 33.7304 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 73ms/step - loss: 536.6387 - val_loss: 152.4569 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 387.2451 - val_loss: 57.1825 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 188.3111 - val_loss: 19.5302 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 114.4194 - val_loss: 41.5053 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 102.4929 - val_loss: 15.5725 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 73ms/step - loss: 85.3098 - val_loss: 9.8276 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 70.7391 - val_loss: 16.8651 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 67.4285 - val_loss: 11.0833 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 58.8396 - val_loss: 5.7798 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 51.6412 - val_loss: 22.2065 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 49.7030 - val_loss: 5.5185 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 73ms/step - loss: 41.4376 - val_loss: 4.5593 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 73ms/step - loss: 38.9224 - val_loss: 4.4720 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 33.8311 - val_loss: 14.6177 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 73ms/step - loss: 31.7345 - val_loss: 4.5170 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 29.5189 - val_loss: 4.6462 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 24.5128 - val_loss: 12.0110 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 27.2786 - val_loss: 9.7918 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 20.8509 - val_loss: 9.4445 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 19.9474 - val_loss: 1.8499 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 16.0964 - val_loss: 2.1507 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 14.9065 - val_loss: 4.0746 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 14.4174 - val_loss: 1.3643 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 13.4686 - val_loss: 1.2367 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 12.4834 - val_loss: 1.2074 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 10.4576 - val_loss: 4.8762 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 9.9904 - val_loss: 2.3661 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 9.0186 - val_loss: 0.8093 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 7.6027 - val_loss: 0.7528 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 7.0828 - val_loss: 1.9957 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 7.4795 - val_loss: 1.6094 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 6.2861 - val_loss: 1.4005 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 5.8326 - val_loss: 3.5256 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 5.8121 - val_loss: 2.7000 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 5.0468 - val_loss: 1.5516 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 4.8042 - val_loss: 0.6889 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 4.3103 - val_loss: 1.3074 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 4.2725 - val_loss: 0.7738 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 4.1835 - val_loss: 1.2574 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 3.8049 - val_loss: 0.7469 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 3.6386 - val_loss: 1.4427 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 3.5660 - val_loss: 3.2461 Epoch 47/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 3.5348 - val_loss: 0.5815 Epoch 48/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 2.9461 - val_loss: 1.2502 Epoch 49/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 3.3271 - val_loss: 2.4640 Epoch 50/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 2.9492 - val_loss: 4.2113 Epoch 51/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 3.2329 - val_loss: 0.4322 Epoch 52/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 2.2804 - val_loss: 4.5992 Epoch 53/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 2.5633 - val_loss: 3.3557 Epoch 54/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 73ms/step - loss: 2.5611 - val_loss: 0.9155 Epoch 55/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 2.0922 - val_loss: 0.4254 Epoch 56/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 2.2412 - val_loss: 0.3239 Epoch 57/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 1.9286 - val_loss: 0.7634 Epoch 58/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 73ms/step - loss: 1.8097 - val_loss: 0.3344 Epoch 59/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 1.8003 - val_loss: 0.5530 Epoch 60/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 73ms/step - loss: 1.7467 - val_loss: 0.2500 Epoch 61/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 1.6733 - val_loss: 0.2223 Epoch 62/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 1.3543 - val_loss: 0.5486 Epoch 63/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 1.4484 - val_loss: 0.2925 Epoch 64/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 1.3129 - val_loss: 0.8146 Epoch 65/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 1.3410 - val_loss: 0.4445 Epoch 66/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 73ms/step - loss: 1.1855 - val_loss: 0.1476 Epoch 67/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 1.1799 - val_loss: 0.2163 Epoch 68/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 73ms/step - loss: 1.0644 - val_loss: 1.0367 Epoch 69/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 1.1678 - val_loss: 1.4934 Epoch 70/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 1.3753 - val_loss: 0.1226 Epoch 71/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 1.0703 - val_loss: 0.6932 Epoch 72/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 1.2698 - val_loss: 0.3904 Epoch 73/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 73ms/step - loss: 2.1696 - val_loss: 2.1317 Epoch 74/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 1.3632 - val_loss: 0.0397 Epoch 75/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.8072 - val_loss: 0.0745 Epoch 76/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.6892 - val_loss: 0.4290 Epoch 77/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.8815 - val_loss: 0.2997 Epoch 78/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.6377 - val_loss: 0.2541 Epoch 79/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.6004 - val_loss: 0.5579 Epoch 80/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.9896 - val_loss: 0.0685 Epoch 81/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.5595 - val_loss: 0.0480 Epoch 82/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.5252 - val_loss: 1.0152 Epoch 83/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.6530 - val_loss: 0.0359 Epoch 84/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.4277 - val_loss: 0.0379 Epoch 85/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.4247 - val_loss: 0.2772 Epoch 86/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.5318 - val_loss: 0.1598 Epoch 87/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 10.7463 - val_loss: 0.1932 Epoch 88/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 2.0304 - val_loss: 0.1506 Epoch 89/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.9633 - val_loss: 0.0303 Epoch 90/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 1.0629 - val_loss: 0.0303 Epoch 91/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.3049 - val_loss: 0.0549 Epoch 92/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.2868 - val_loss: 0.0311 Epoch 93/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.2720 - val_loss: 0.0297 Epoch 94/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.3549 - val_loss: 0.0241 Epoch 95/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.2720 - val_loss: 0.0225 Epoch 96/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.2358 - val_loss: 0.0248 Epoch 97/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.2260 - val_loss: 0.0290 Epoch 98/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.2389 - val_loss: 0.0304 Epoch 99/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.2231 - val_loss: 0.0248 Epoch 100/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 74ms/step - loss: 0.2077 - val_loss: 0.0230 53/53 ━━━━━━━━━━━━━━━━━━━━ 1s 19ms/step Sample raw predictions (after inverse transform and clipping): [3.9986901 0. 0.18433712 4.056968 0. ] RMSE = 2.2376428 Validation R-squared for item 2212: -0.9117189645767212 52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 17ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=6.101165771484375 True y_val range (after inverse transform): min=0.0, max=18.0
-----------------------------------
Current item is 2467
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=9
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.8888888888888888
x_eval_time shape before reshape: (1708, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1988, 14)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (209, 20)
store_x_eval.shape: (220, 20)
store_x_eval.shape: (213, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (182, 20)
Model: "sequential_98"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_197 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_102 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_198 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_103 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_97 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 77ms/step - loss: 9249.5352 - val_loss: 210.9825 Epoch 2/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 304.1246 - val_loss: 22.1171 Epoch 3/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 33.5302 - val_loss: 7.7710 Epoch 4/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 9.7997 - val_loss: 1.7165 Epoch 5/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 5.7006 - val_loss: 1.5319 Epoch 6/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 3.7727 - val_loss: 0.6032 Epoch 7/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 2.3605 - val_loss: 0.0444 Epoch 8/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 1.4063 - val_loss: 0.0256 Epoch 9/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.9961 - val_loss: 0.0190 Epoch 10/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 0.6394 - val_loss: 0.0180 Epoch 11/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.4089 - val_loss: 0.0300 Epoch 12/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.5264 - val_loss: 0.0105 Epoch 13/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.4333 - val_loss: 0.0246 Epoch 14/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.3833 - val_loss: 0.0092 Epoch 15/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.2669 - val_loss: 0.0114 Epoch 16/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.4744 - val_loss: 0.0786 Epoch 17/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.6441 - val_loss: 0.1019 Epoch 18/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.6838 - val_loss: 0.3421 Epoch 19/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.7510 - val_loss: 0.0827 Epoch 20/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.4266 - val_loss: 0.0212 Epoch 21/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.4069 - val_loss: 0.1917 Epoch 22/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.1947 - val_loss: 0.0168 Epoch 23/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.2018 - val_loss: 0.0269 Epoch 24/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 0.2975 - val_loss: 0.0175 52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 19ms/step Sample raw predictions (after inverse transform and clipping): [0.754617 0.8843739 0.75549024 0.76333183 1.0944864 ] RMSE = 1.1438525 Validation R-squared for item 2467: -0.4422413110733032 54/54 ━━━━━━━━━━━━━━━━━━━━ 1s 17ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=15.418377876281738 True y_val range (after inverse transform): min=0.0, max=8.0
-----------------------------------
Current item is 1489
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=11
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.6363636363636364
x_eval_time shape before reshape: (1581, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1861, 14)
store_x_eval.shape: (207, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (174, 20)
store_x_eval.shape: (175, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (185, 20)
Model: "sequential_99"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_199 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_104 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_200 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_105 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_98 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 78ms/step - loss: 7672.4600 - val_loss: 53.0347 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 256.7303 - val_loss: 7.7590 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 66.4652 - val_loss: 5.0845 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 27.5519 - val_loss: 3.0844 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 15.1070 - val_loss: 1.3465 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 7.3645 - val_loss: 0.9973 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 5.9534 - val_loss: 0.2510 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 3.2752 - val_loss: 1.0692 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 2.2312 - val_loss: 0.5434 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 1.4472 - val_loss: 0.4504 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 75ms/step - loss: 1.3015 - val_loss: 0.2896 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.9176 - val_loss: 0.2507 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 0.6470 - val_loss: 0.1283 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 0.5782 - val_loss: 0.1067 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 0.4492 - val_loss: 0.0284 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 1.3571 - val_loss: 0.0300 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 0.6136 - val_loss: 0.2301 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 0.6118 - val_loss: 0.0653 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 0.4625 - val_loss: 0.1011 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 0.3544 - val_loss: 0.1276 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.2963 - val_loss: 0.1083 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 0.2818 - val_loss: 0.1005 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.2524 - val_loss: 0.0331 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.1958 - val_loss: 0.0368 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 0.1834 - val_loss: 0.0715 52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step Sample raw predictions (after inverse transform and clipping): [1.4311068 1.4624445 1.3558273 0.10668732 1.0633548 ] RMSE = 1.0941626 Validation R-squared for item 1489: -1.221785545349121 50/50 ━━━━━━━━━━━━━━━━━━━━ 1s 17ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=3.040214776992798 True y_val range (after inverse transform): min=0.0, max=7.0
-----------------------------------
Current item is 2365
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=8
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.5
x_eval_time shape before reshape: (1734, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (2014, 14)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (215, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (213, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (215, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (215, 20)
store_x_eval.shape: (183, 20)
Model: "sequential_100"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_201 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_106 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_202 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_107 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_99 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 12s 80ms/step - loss: 19351.8379 - val_loss: 717.8820 Epoch 2/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 2211.5840 - val_loss: 170.8561 Epoch 3/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 666.6846 - val_loss: 38.9263 Epoch 4/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 223.2035 - val_loss: 11.7611 Epoch 5/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 107.5310 - val_loss: 4.2626 Epoch 6/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 69.4284 - val_loss: 1.9447 Epoch 7/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 46.7131 - val_loss: 1.1654 Epoch 8/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 32.9381 - val_loss: 0.8632 Epoch 9/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 24.0009 - val_loss: 0.5804 Epoch 10/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 17.9426 - val_loss: 0.5417 Epoch 11/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 13.3386 - val_loss: 0.3123 Epoch 12/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 10.0706 - val_loss: 0.5559 Epoch 13/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 7.6576 - val_loss: 0.1100 Epoch 14/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 3.7758 - val_loss: 0.0703 Epoch 15/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 1.7915 - val_loss: 0.0356 Epoch 16/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.8454 - val_loss: 0.0230 Epoch 17/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.4594 - val_loss: 0.0078 Epoch 18/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.5934 - val_loss: 0.0529 Epoch 19/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.2378 - val_loss: 0.0829 Epoch 20/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.2745 - val_loss: 0.0147 Epoch 21/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.1707 - val_loss: 0.0116 Epoch 22/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.0994 - val_loss: 0.5190 Epoch 23/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.1954 - val_loss: 0.0225 Epoch 24/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.1341 - val_loss: 0.0091 Epoch 25/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.0714 - val_loss: 1.0447 Epoch 26/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.2614 - val_loss: 0.0297 Epoch 27/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.0841 - val_loss: 0.0204 53/53 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step Sample raw predictions (after inverse transform and clipping): [0.5446452 0.70602715 0.04151163 0.49380937 0.055029 ] RMSE = 0.48185068 Validation R-squared for item 2365: -2.038820266723633 55/55 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=3.197000741958618 True y_val range (after inverse transform): min=0.0, max=4.0
-----------------------------------
Current item is 2260
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=7
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.7142857142857142
x_eval_time shape before reshape: (1716, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1996, 14)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (178, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (217, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (226, 20)
store_x_eval.shape: (212, 20)
Model: "sequential_101"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_203 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_108 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_204 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_109 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_100 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 10s 81ms/step - loss: 48576.5898 - val_loss: 3658.3506 Epoch 2/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 5587.9824 - val_loss: 62.7286 Epoch 3/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 950.8148 - val_loss: 29.5124 Epoch 4/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 377.9333 - val_loss: 25.8732 Epoch 5/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 250.7539 - val_loss: 17.6715 Epoch 6/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 230.8773 - val_loss: 19.0718 Epoch 7/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 171.1406 - val_loss: 17.2950 Epoch 8/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 124.1863 - val_loss: 13.1629 Epoch 9/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 92.2368 - val_loss: 31.1964 Epoch 10/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 67.4614 - val_loss: 11.5431 Epoch 11/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 54.3078 - val_loss: 14.7245 Epoch 12/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 43.0303 - val_loss: 5.1507 Epoch 13/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 33.7806 - val_loss: 6.2509 Epoch 14/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 26.6440 - val_loss: 3.8826 Epoch 15/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 22.1484 - val_loss: 3.2280 Epoch 16/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 19.3375 - val_loss: 1.7064 Epoch 17/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 15.0661 - val_loss: 2.8165 Epoch 18/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 11.4576 - val_loss: 1.9815 Epoch 19/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 10.6153 - val_loss: 1.2500 Epoch 20/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 9.0965 - val_loss: 1.4935 Epoch 21/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 8.0189 - val_loss: 0.9324 Epoch 22/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 6.8562 - val_loss: 0.8604 Epoch 23/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 9.0487 - val_loss: 1.1469 Epoch 24/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 7.3553 - val_loss: 1.1568 Epoch 25/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 6.6919 - val_loss: 0.4846 Epoch 26/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 5.1497 - val_loss: 1.1250 Epoch 27/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 5.5060 - val_loss: 1.7040 Epoch 28/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 4.0306 - val_loss: 0.2297 Epoch 29/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 4.1413 - val_loss: 1.5391 Epoch 30/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 3.5974 - val_loss: 0.5323 Epoch 31/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 3.3805 - val_loss: 1.4071 Epoch 32/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 3.3210 - val_loss: 0.4457 Epoch 33/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 2.9297 - val_loss: 0.3496 Epoch 34/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 2.6777 - val_loss: 0.3382 Epoch 35/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 1.9179 - val_loss: 0.7590 Epoch 36/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 1.8158 - val_loss: 0.2595 Epoch 37/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 1.8162 - val_loss: 0.0586 Epoch 38/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 2.1547 - val_loss: 0.0482 Epoch 39/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 2.1730 - val_loss: 0.5808 Epoch 40/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 1.6750 - val_loss: 0.0576 Epoch 41/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.9971 - val_loss: 0.3583 Epoch 42/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 2.2466 - val_loss: 0.0742 Epoch 43/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.9040 - val_loss: 0.0172 Epoch 44/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.8953 - val_loss: 0.2015 Epoch 45/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.8143 - val_loss: 0.1922 Epoch 46/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.7342 - val_loss: 0.3227 Epoch 47/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.7920 - val_loss: 0.9320 Epoch 48/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 0.6256 - val_loss: 0.0996 Epoch 49/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.4459 - val_loss: 0.0248 Epoch 50/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.6665 - val_loss: 0.0405 Epoch 51/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.4696 - val_loss: 0.0137 Epoch 52/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.5855 - val_loss: 0.0207 Epoch 53/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.5118 - val_loss: 0.8898 Epoch 54/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.5166 - val_loss: 0.0203 Epoch 55/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.6186 - val_loss: 0.0351 Epoch 56/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 1.2574 - val_loss: 0.0984 Epoch 57/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 1.1155 - val_loss: 0.1594 Epoch 58/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 76ms/step - loss: 0.7621 - val_loss: 0.3552 Epoch 59/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.4781 - val_loss: 0.0949 Epoch 60/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.7398 - val_loss: 0.2511 Epoch 61/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 8s 77ms/step - loss: 0.6626 - val_loss: 0.0162 53/53 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step Sample raw predictions (after inverse transform and clipping): [0.95859814 0.575128 0.61649907 0.3565337 0.6526011 ] RMSE = 0.7649964 Validation R-squared for item 2260: -1.2166862487792969 54/54 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=2.758286476135254 True y_val range (after inverse transform): min=0.0, max=5.0
-----------------------------------
Current item is 1518
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=37
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.2702702702702703
x_eval_time shape before reshape: (1619, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1899, 14)
store_x_eval.shape: (177, 20)
store_x_eval.shape: (209, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (175, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (205, 20)
store_x_eval.shape: (168, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (174, 20)
Model: "sequential_102"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_205 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_110 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_206 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_111 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_101 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 80ms/step - loss: 28168.2852 - val_loss: 254.2339 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 2636.2229 - val_loss: 102.1992 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 588.9793 - val_loss: 58.8910 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 303.7220 - val_loss: 11.9821 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 172.9925 - val_loss: 3.5657 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 128.0503 - val_loss: 2.3995 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 70.0043 - val_loss: 2.1790 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 28.6356 - val_loss: 0.6924 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 17.1386 - val_loss: 0.8472 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 80ms/step - loss: 14.5312 - val_loss: 0.5145 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 12.5162 - val_loss: 0.4852 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 12.3351 - val_loss: 0.2900 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 6.9127 - val_loss: 0.2304 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 6.8316 - val_loss: 0.2942 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 6.5038 - val_loss: 0.2542 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 7.6964 - val_loss: 0.2580 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 5.2537 - val_loss: 0.1344 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 4.6520 - val_loss: 0.2165 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 3.6085 - val_loss: 0.0819 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 4.6730 - val_loss: 0.0517 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 3.0118 - val_loss: 0.0937 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 4.0927 - val_loss: 0.0338 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 2.4578 - val_loss: 0.0096 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 3.6347 - val_loss: 0.0097 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 4.1039 - val_loss: 0.3120 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 1.0016 - val_loss: 0.0112 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 1.4586 - val_loss: 0.0983 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 1.9907 - val_loss: 0.0068 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 1.7559 - val_loss: 0.0067 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.8311 - val_loss: 0.1449 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 1.2146 - val_loss: 0.0355 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 1.1079 - val_loss: 0.0103 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.5400 - val_loss: 0.1072 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.9629 - val_loss: 0.0255 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.9279 - val_loss: 0.0561 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 1.4441 - val_loss: 0.0470 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.6798 - val_loss: 0.0291 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 1.2575 - val_loss: 0.0031 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.5920 - val_loss: 0.0090 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.3342 - val_loss: 0.0536 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.3200 - val_loss: 0.0063 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.2110 - val_loss: 0.0012 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.2297 - val_loss: 0.0053 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.2453 - val_loss: 0.0014 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.1361 - val_loss: 0.0016 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.1401 - val_loss: 0.0113 Epoch 47/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.2811 - val_loss: 0.0028 Epoch 48/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.0908 - val_loss: 0.0301 Epoch 49/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.5022 - val_loss: 0.0513 Epoch 50/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.6103 - val_loss: 0.0035 Epoch 51/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 0.0915 - val_loss: 0.0204 Epoch 52/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.2164 - val_loss: 0.0054 52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step Sample raw predictions (after inverse transform and clipping): [0.13276288 0. 0.12255774 0. 0. ] RMSE = 1.11512 Validation R-squared for item 1518: 0.03085637092590332 51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=1.5972182750701904 True y_val range (after inverse transform): min=0.0, max=10.0
-----------------------------------
Current item is 2215
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=10
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.7000000000000001
x_eval_time shape before reshape: (1638, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1918, 14)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (208, 20)
store_x_eval.shape: (180, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (188, 20)
Model: "sequential_103"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_207 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_112 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_208 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_113 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_102 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 82ms/step - loss: 4645.4214 - val_loss: 13.0466 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 120.0400 - val_loss: 4.4863 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 26.9371 - val_loss: 1.7896 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 13.4880 - val_loss: 2.5927 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 7.6164 - val_loss: 0.1976 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 6.4936 - val_loss: 1.2857 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 5.3539 - val_loss: 0.1656 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 3.1342 - val_loss: 0.7137 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 3.0016 - val_loss: 0.0844 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 2.3135 - val_loss: 0.0360 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 1.2640 - val_loss: 0.0466 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 1.1337 - val_loss: 0.0512 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.7733 - val_loss: 0.0887 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.6675 - val_loss: 0.0574 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.5773 - val_loss: 0.0731 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.5111 - val_loss: 0.3907 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.8922 - val_loss: 0.0393 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.4145 - val_loss: 0.0335 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.5934 - val_loss: 0.0426 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.4455 - val_loss: 0.0183 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.3629 - val_loss: 0.0583 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.3091 - val_loss: 0.0174 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.2839 - val_loss: 0.6620 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.3292 - val_loss: 0.1936 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.2593 - val_loss: 0.0088 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.3407 - val_loss: 0.4215 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.4606 - val_loss: 0.0105 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1931 - val_loss: 0.0202 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.2361 - val_loss: 0.0669 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1782 - val_loss: 0.0084 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1254 - val_loss: 0.5630 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.1851 - val_loss: 0.0078 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.1780 - val_loss: 0.2528 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1525 - val_loss: 0.0199 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.2010 - val_loss: 0.0047 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.3022 - val_loss: 0.0254 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.1606 - val_loss: 0.0070 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1816 - val_loss: 0.0175 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1542 - val_loss: 0.0153 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1658 - val_loss: 0.0306 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1324 - val_loss: 0.0471 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1229 - val_loss: 0.0141 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.3362 - val_loss: 1.3314 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 1.1294 - val_loss: 0.0103 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.0422 - val_loss: 0.0235 51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step Sample raw predictions (after inverse transform and clipping): [0.38735405 0.34696692 0.3587319 0.18122476 0.1310397 ] RMSE = 0.77052706 Validation R-squared for item 2215: -0.058983445167541504 52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 19ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=2.1956982612609863 True y_val range (after inverse transform): min=0.0, max=7.0
-----------------------------------
Current item is 2302
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=9
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.7777777777777777
x_eval_time shape before reshape: (1595, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1875, 14)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (210, 20)
store_x_eval.shape: (176, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (178, 20)
store_x_eval.shape: (175, 20)
Model: "sequential_104"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_209 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_114 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_210 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_115 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_103 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 80ms/step - loss: 36783.6797 - val_loss: 21775.6113 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 48349.5000 - val_loss: 806.7084 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 2414.4502 - val_loss: 219.2616 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 768.2894 - val_loss: 94.6791 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 332.0251 - val_loss: 16.7730 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 112.8234 - val_loss: 15.0475 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 62.1705 - val_loss: 8.2675 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 44.2032 - val_loss: 2.9688 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 23.0801 - val_loss: 1.5646 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 14.9223 - val_loss: 1.0083 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 7.1313 - val_loss: 0.4796 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 9.2141 - val_loss: 0.2242 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 6.1858 - val_loss: 0.1890 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 5.0102 - val_loss: 0.1498 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 3.3985 - val_loss: 0.1708 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 79ms/step - loss: 3.5042 - val_loss: 0.2163 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 2.7735 - val_loss: 0.2468 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 1.6163 - val_loss: 0.1178 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 1.7499 - val_loss: 0.0728 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.8258 - val_loss: 0.0369 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 1.4502 - val_loss: 0.0758 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 2.3290 - val_loss: 0.0480 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 1.4094 - val_loss: 0.0591 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 1.7964 - val_loss: 0.1051 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 1.5316 - val_loss: 0.0432 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 1.0390 - val_loss: 0.0617 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.8185 - val_loss: 0.0426 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.5901 - val_loss: 0.0705 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.5312 - val_loss: 0.1554 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 8s 78ms/step - loss: 0.4164 - val_loss: 0.0520 53/53 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step Sample raw predictions (after inverse transform and clipping): [0.73134065 0. 0.37978408 0.17293084 0. ] RMSE = 0.89458644 Validation R-squared for item 2302: -1.0573971271514893 50/50 ━━━━━━━━━━━━━━━━━━━━ 1s 18ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=7.224201679229736 True y_val range (after inverse transform): min=0.0, max=7.0
-----------------------------------
Current item is 2374
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=10
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.7000000000000001
x_eval_time shape before reshape: (1625, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1905, 14)
store_x_eval.shape: (167, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (166, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (181, 20)
Model: "sequential_105"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_211 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_116 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_212 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_117 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_104 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 82ms/step - loss: 26126.0391 - val_loss: 762.1906 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 1899.4292 - val_loss: 357.8877 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 815.6359 - val_loss: 111.7471 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 334.9939 - val_loss: 51.3484 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 214.7848 - val_loss: 19.6026 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 123.9470 - val_loss: 15.4822 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 73.9559 - val_loss: 7.8372 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 54.0721 - val_loss: 2.5749 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 23.2246 - val_loss: 1.2953 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 14.5510 - val_loss: 0.9505 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 8.0738 - val_loss: 0.6394 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 6.9885 - val_loss: 0.3841 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 4.7139 - val_loss: 0.2458 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 2.9241 - val_loss: 9.8497 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 3.2612 - val_loss: 0.1631 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 1.5156 - val_loss: 0.1123 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 1.3735 - val_loss: 0.0630 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 1.0836 - val_loss: 0.0407 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 0.8269 - val_loss: 1.1462 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.8654 - val_loss: 0.0308 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.6427 - val_loss: 0.0430 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.5152 - val_loss: 1.4687 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.6018 - val_loss: 0.2635 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.5180 - val_loss: 0.0718 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.3381 - val_loss: 0.1419 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.3137 - val_loss: 0.1214 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.3266 - val_loss: 0.0177 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.3164 - val_loss: 0.0667 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.2888 - val_loss: 0.1725 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.3159 - val_loss: 0.0157 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.2401 - val_loss: 0.0223 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.3065 - val_loss: 0.4401 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.2827 - val_loss: 0.0166 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1989 - val_loss: 0.1114 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.3168 - val_loss: 0.0178 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1512 - val_loss: 0.0469 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1595 - val_loss: 0.0328 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1825 - val_loss: 0.0113 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1451 - val_loss: 0.0215 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1398 - val_loss: 0.1041 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.2884 - val_loss: 0.0163 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1341 - val_loss: 0.0103 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1502 - val_loss: 0.0092 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.2465 - val_loss: 0.0382 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.0996 - val_loss: 0.0262 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.0987 - val_loss: 0.0482 Epoch 47/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.0667 - val_loss: 0.0092 Epoch 48/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.0839 - val_loss: 0.0180 Epoch 49/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.1824 - val_loss: 0.0311 Epoch 50/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 80ms/step - loss: 0.0866 - val_loss: 0.0653 Epoch 51/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.0862 - val_loss: 0.0094 Epoch 52/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.1345 - val_loss: 0.1842 Epoch 53/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 0.0782 - val_loss: 0.0674 50/50 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step Sample raw predictions (after inverse transform and clipping): [0.18363729 0.8050488 0.5737694 1.1469452 1.8379602 ] RMSE = 1.0374782 Validation R-squared for item 2374: -0.2908381223678589 51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 19ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=2.136575222015381 True y_val range (after inverse transform): min=0.0, max=7.0
-----------------------------------
Current item is 1251
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=26
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.46153846153846156
x_eval_time shape before reshape: (1686, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1966, 14)
store_x_eval.shape: (216, 20)
store_x_eval.shape: (180, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (180, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (209, 20)
Model: "sequential_106"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_213 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_118 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_214 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_119 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_105 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 84ms/step - loss: 198958.6406 - val_loss: 24660.7441 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 65067.7812 - val_loss: 493.2159 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 6829.2407 - val_loss: 157.7820 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 1772.8585 - val_loss: 223.5141 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 1119.9945 - val_loss: 299.5569 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 685.0761 - val_loss: 98.6058 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 488.7709 - val_loss: 190.4696 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 294.9742 - val_loss: 40.8018 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 249.2172 - val_loss: 28.5109 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 172.5585 - val_loss: 12.7425 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 145.8395 - val_loss: 13.6653 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 111.0307 - val_loss: 8.4974 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 96.5358 - val_loss: 11.9968 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 83.7300 - val_loss: 17.2608 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 73.4851 - val_loss: 75.0582 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 75.6705 - val_loss: 5.8544 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 61.7437 - val_loss: 7.7614 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 55.6963 - val_loss: 5.7257 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 55.5335 - val_loss: 5.9441 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 44.9989 - val_loss: 15.1849 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 40.8252 - val_loss: 11.2726 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 37.0253 - val_loss: 4.9604 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 34.0081 - val_loss: 4.5218 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 34.8975 - val_loss: 3.2500 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 29.1669 - val_loss: 27.3662 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 29.2797 - val_loss: 2.8317 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 25.5027 - val_loss: 6.8082 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 24.7392 - val_loss: 2.9100 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 26.2334 - val_loss: 10.4113 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 22.5763 - val_loss: 22.8369 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 26.3348 - val_loss: 16.1530 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 20.9411 - val_loss: 2.1998 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 17.5564 - val_loss: 2.1519 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 102.6731 - val_loss: 3.0032 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 17.0103 - val_loss: 1.9555 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 17.5342 - val_loss: 11.5386 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 16.9134 - val_loss: 1.7581 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 12.9218 - val_loss: 1.7507 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 12.5564 - val_loss: 4.4047 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 13.1820 - val_loss: 1.4098 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 9.1679 - val_loss: 1.2403 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 10.7320 - val_loss: 10.6720 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 11.8756 - val_loss: 2.8240 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 7.7198 - val_loss: 10.8601 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 7.3677 - val_loss: 0.9620 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 8.0571 - val_loss: 12.1671 Epoch 47/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 7.4508 - val_loss: 2.2071 Epoch 48/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 5.9562 - val_loss: 1.8222 Epoch 49/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 6.4859 - val_loss: 7.1637 Epoch 50/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 7.0989 - val_loss: 1.3053 Epoch 51/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 6.2648 - val_loss: 3.9454 Epoch 52/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 4.8373 - val_loss: 0.5605 Epoch 53/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 5.7271 - val_loss: 0.3936 Epoch 54/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 3.8442 - val_loss: 4.0144 Epoch 55/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 4.3560 - val_loss: 3.7193 Epoch 56/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 5.0977 - val_loss: 27.8877 Epoch 57/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 8.9851 - val_loss: 1.6578 Epoch 58/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 3.9492 - val_loss: 3.7848 Epoch 59/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 4.6180 - val_loss: 1.5677 Epoch 60/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 4.4640 - val_loss: 0.2999 Epoch 61/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 3.4251 - val_loss: 0.7334 Epoch 62/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 2.4335 - val_loss: 8.3348 Epoch 63/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 4.7043 - val_loss: 7.9175 Epoch 64/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 2.8513 - val_loss: 5.3727 Epoch 65/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 4.0095 - val_loss: 3.1687 Epoch 66/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 2.6600 - val_loss: 0.3541 Epoch 67/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 2.7761 - val_loss: 12.9142 Epoch 68/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 81ms/step - loss: 5.3904 - val_loss: 0.2808 Epoch 69/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 3.1049 - val_loss: 1.4748 Epoch 70/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 1.7022 - val_loss: 6.5131 Epoch 71/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 4.4127 - val_loss: 0.9225 Epoch 72/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 1.5083 - val_loss: 2.8427 Epoch 73/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 2.9067 - val_loss: 5.1120 Epoch 74/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 2.5581 - val_loss: 15.6357 Epoch 75/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 5.7102 - val_loss: 0.0899 Epoch 76/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 1.8448 - val_loss: 0.4580 Epoch 77/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 1.7421 - val_loss: 13.9468 Epoch 78/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 4.3565 - val_loss: 0.7292 Epoch 79/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 2.0403 - val_loss: 2.2832 Epoch 80/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 5.6222 - val_loss: 3.4456 Epoch 81/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 1.4468 - val_loss: 0.4202 Epoch 82/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 1.2842 - val_loss: 3.4947 Epoch 83/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 2.1503 - val_loss: 0.5687 Epoch 84/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 1.4911 - val_loss: 0.5599 Epoch 85/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 1.1094 - val_loss: 3.6159 52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step Sample raw predictions (after inverse transform and clipping): [3.2665732 0. 0. 7.0570183 0. ] RMSE = 4.3291354 Validation R-squared for item 1251: -3.7527356147766113 53/53 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=15.979684829711914 True y_val range (after inverse transform): min=0.0, max=12.0
-----------------------------------
Current item is 2163
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=12
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.6666666666666666
x_eval_time shape before reshape: (1637, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1917, 14)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (173, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (198, 20)
Model: "sequential_107"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_215 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_120 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_216 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_121 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_106 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 84ms/step - loss: 11166.2637 - val_loss: 162.7639 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 476.2951 - val_loss: 3.2475 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 35.8252 - val_loss: 0.6841 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 10.9624 - val_loss: 0.5782 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 6.3534 - val_loss: 7.2067 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 5.5963 - val_loss: 0.0726 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 2.5653 - val_loss: 0.2542 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 1.6061 - val_loss: 0.4738 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 1.1392 - val_loss: 0.0192 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 1.2895 - val_loss: 8.5263 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 2.1536 - val_loss: 0.0060 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.4467 - val_loss: 0.2350 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.3328 - val_loss: 0.2186 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 86ms/step - loss: 0.3399 - val_loss: 0.1296 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.3041 - val_loss: 0.0165 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.4876 - val_loss: 0.0288 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.3039 - val_loss: 0.8052 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.4339 - val_loss: 0.5100 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.3816 - val_loss: 1.8579 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.5881 - val_loss: 0.1072 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 0.4374 - val_loss: 0.0089 54/54 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step Sample raw predictions (after inverse transform and clipping): [0. 0. 0. 0. 0.] RMSE = 0.73363996 Validation R-squared for item 2163: -0.18651211261749268 52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 20ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=2.7877354621887207 True y_val range (after inverse transform): min=0.0, max=8.0
-----------------------------------
Current item is 2482
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=10
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.4
x_eval_time shape before reshape: (1609, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1889, 14)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (174, 20)
store_x_eval.shape: (177, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (197, 20)
Model: "sequential_108"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_217 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_122 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_218 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_123 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_107 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 84ms/step - loss: 62949.2891 - val_loss: 15878.2295 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 12563.6553 - val_loss: 955.4444 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 3039.5222 - val_loss: 411.9238 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 1269.3661 - val_loss: 114.8837 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 569.7228 - val_loss: 82.5057 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 82ms/step - loss: 820.9339 - val_loss: 57.4599 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 336.9856 - val_loss: 26.7220 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 230.5864 - val_loss: 18.8633 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 151.3792 - val_loss: 11.6601 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 106.7263 - val_loss: 8.1631 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 87.6728 - val_loss: 6.5379 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 75.8701 - val_loss: 6.1110 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 55.3970 - val_loss: 5.9275 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 43.2164 - val_loss: 3.8283 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 35.4816 - val_loss: 2.4329 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 31.2489 - val_loss: 2.0393 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 23.4499 - val_loss: 1.6365 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 21.6298 - val_loss: 1.8035 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 18.1926 - val_loss: 1.2985 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 15.8595 - val_loss: 0.9764 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 14.3360 - val_loss: 2.1665 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 13.5899 - val_loss: 0.9781 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 11.6351 - val_loss: 0.8647 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 10.3888 - val_loss: 1.0711 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 85ms/step - loss: 9.5701 - val_loss: 0.6870 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 10.2550 - val_loss: 0.7303 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 9.1173 - val_loss: 0.4356 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 8.6127 - val_loss: 0.6266 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 7.8844 - val_loss: 2.1800 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 6.8211 - val_loss: 0.8884 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 6.9258 - val_loss: 0.5623 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 5.4083 - val_loss: 0.2992 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 4.9774 - val_loss: 0.6483 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 4.9454 - val_loss: 0.4409 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 3.8183 - val_loss: 0.4192 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 4.4337 - val_loss: 0.2810 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 3.8512 - val_loss: 0.6076 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 3.2678 - val_loss: 1.5353 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 3.1630 - val_loss: 0.3480 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 2.7755 - val_loss: 0.4863 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 2.4300 - val_loss: 0.3660 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 3.0885 - val_loss: 0.5167 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 2.3190 - val_loss: 0.3570 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 2.1299 - val_loss: 0.6472 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 1.8296 - val_loss: 0.6081 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 1.8675 - val_loss: 0.2893 53/53 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step Sample raw predictions (after inverse transform and clipping): [0. 4.4759107 3.6678462 0. 0. ] RMSE = 3.1656024 Validation R-squared for item 2482: -41.6834602355957 51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 19ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=14.068412780761719 True y_val range (after inverse transform): min=0.0, max=4.0
-----------------------------------
Current item is 1119
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=17
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.0588235294117647
x_eval_time shape before reshape: (1705, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1985, 14)
store_x_eval.shape: (227, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (229, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (177, 20)
store_x_eval.shape: (206, 20)
store_x_eval.shape: (190, 20)
Model: "sequential_109"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_219 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_124 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_220 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_125 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_108 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 10s 85ms/step - loss: 4667.2827 - val_loss: 40.3608 Epoch 2/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 149.6156 - val_loss: 3.3145 Epoch 3/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 18.2869 - val_loss: 0.7487 Epoch 4/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 4.5521 - val_loss: 0.0388 Epoch 5/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.6265 - val_loss: 0.0259 Epoch 6/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.3078 - val_loss: 0.0178 Epoch 7/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 0.4413 - val_loss: 0.0161 Epoch 8/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 0.1308 - val_loss: 0.0194 Epoch 9/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 0.2206 - val_loss: 0.0233 Epoch 10/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 85ms/step - loss: 0.0847 - val_loss: 0.0203 Epoch 11/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 0.0813 - val_loss: 0.1220 Epoch 12/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.0918 - val_loss: 0.0263 Epoch 13/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 0.0682 - val_loss: 0.0426 Epoch 14/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.0500 - val_loss: 0.0148 Epoch 15/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.0452 - val_loss: 0.0243 Epoch 16/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 0.0580 - val_loss: 0.0941 Epoch 17/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 0.0386 - val_loss: 0.0148 Epoch 18/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.0373 - val_loss: 0.1040 Epoch 19/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.1083 - val_loss: 0.0274 Epoch 20/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.0357 - val_loss: 1.0668 Epoch 21/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.1984 - val_loss: 0.0194 Epoch 22/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 0.0299 - val_loss: 0.0352 Epoch 23/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 0.2334 - val_loss: 0.0148 Epoch 24/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.0561 - val_loss: 0.0187 Epoch 25/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 0.0395 - val_loss: 0.0152 Epoch 26/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.0178 - val_loss: 0.0144 Epoch 27/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.0250 - val_loss: 0.0151 Epoch 28/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.0561 - val_loss: 0.0201 Epoch 29/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.0336 - val_loss: 0.0183 Epoch 30/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.0371 - val_loss: 0.0185 Epoch 31/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.0141 - val_loss: 0.0162 Epoch 32/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 0.1812 - val_loss: 0.0179 Epoch 33/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.0182 - val_loss: 0.0255 Epoch 34/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 0.0311 - val_loss: 0.0181 Epoch 35/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 83ms/step - loss: 0.0360 - val_loss: 0.0892 Epoch 36/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 9s 84ms/step - loss: 0.0321 - val_loss: 0.0158 52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step Sample raw predictions (after inverse transform and clipping): [1.6317894 1.7522495 1.4531772 1.7346601 1.4845957] RMSE = 1.8835926 Validation R-squared for item 1119: -0.05382728576660156 54/54 ━━━━━━━━━━━━━━━━━━━━ 1s 21ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=3.1098947525024414 True y_val range (after inverse transform): min=0.0, max=18.0
-----------------------------------
Current item is 2190
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=30
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.43333333333333335
x_eval_time shape before reshape: (1632, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1912, 14)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (174, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (205, 20)
store_x_eval.shape: (192, 20)
Model: "sequential_110"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_221 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_126 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_222 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_127 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_109 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 88ms/step - loss: 39265.3594 - val_loss: 1015.3320 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 3172.5127 - val_loss: 272.7464 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 666.9436 - val_loss: 159.3783 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 86ms/step - loss: 349.3012 - val_loss: 114.4419 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 157.0905 - val_loss: 5.3141 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 90ms/step - loss: 39.1010 - val_loss: 3.2479 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 22.3864 - val_loss: 1.4006 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 27.1769 - val_loss: 59.0822 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 86.9032 - val_loss: 12.1451 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 69.7718 - val_loss: 14.0733 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 26.5413 - val_loss: 2.6114 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 10.7584 - val_loss: 0.8082 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 6.9248 - val_loss: 0.6288 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 3.9365 - val_loss: 0.2698 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 2.5200 - val_loss: 0.4146 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 2.1708 - val_loss: 0.2064 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 1.2690 - val_loss: 1.6892 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 1.1132 - val_loss: 0.0732 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.7962 - val_loss: 0.1622 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.8259 - val_loss: 0.0353 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.6548 - val_loss: 0.1253 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.4723 - val_loss: 0.3059 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.3959 - val_loss: 0.4279 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.4237 - val_loss: 0.1511 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.4322 - val_loss: 1.3108 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.4245 - val_loss: 0.0247 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.3420 - val_loss: 0.0377 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.2814 - val_loss: 0.1904 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.4447 - val_loss: 0.0422 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.3095 - val_loss: 1.4548 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 1.1277 - val_loss: 0.0027 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.1554 - val_loss: 0.0404 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.4064 - val_loss: 0.0398 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.6038 - val_loss: 0.0213 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.2780 - val_loss: 0.0565 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 6.2050 - val_loss: 0.0227 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.1937 - val_loss: 0.0182 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.1173 - val_loss: 0.0216 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.2156 - val_loss: 0.0158 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.1013 - val_loss: 0.0045 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.1007 - val_loss: 0.0022 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.0491 - val_loss: 0.0079 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.0304 - val_loss: 0.0021 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.0278 - val_loss: 0.0059 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.0363 - val_loss: 0.0018 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.0401 - val_loss: 0.0684 Epoch 47/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.1111 - val_loss: 0.0091 Epoch 48/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.0396 - val_loss: 0.2533 Epoch 49/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.0659 - val_loss: 1.6396 Epoch 50/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.4599 - val_loss: 0.0049 Epoch 51/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.1956 - val_loss: 0.0377 Epoch 52/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.0880 - val_loss: 0.0793 Epoch 53/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.0871 - val_loss: 0.0015 Epoch 54/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.0340 - val_loss: 0.2282 Epoch 55/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.4689 - val_loss: 0.0542 Epoch 56/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.0911 - val_loss: 9.1127e-04 Epoch 57/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.0152 - val_loss: 0.0174 Epoch 58/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.1222 - val_loss: 0.0019 Epoch 59/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.0921 - val_loss: 0.0071 Epoch 60/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.4162 - val_loss: 0.0011 Epoch 61/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 87ms/step - loss: 0.0357 - val_loss: 0.0682 Epoch 62/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.0609 - val_loss: 0.0950 Epoch 63/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.0602 - val_loss: 0.0353 Epoch 64/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.1805 - val_loss: 0.1106 Epoch 65/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.1051 - val_loss: 0.0043 Epoch 66/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.1306 - val_loss: 0.0114 52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 25ms/step Sample raw predictions (after inverse transform and clipping): [0.83503187 0.54945946 0.92360103 0.73239404 0.43584126] RMSE = 0.9302448 Validation R-squared for item 2190: -0.11616659164428711 51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=1.4678949117660522 True y_val range (after inverse transform): min=0.0, max=13.0
-----------------------------------
Current item is 1071
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=24
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.625
x_eval_time shape before reshape: (1540, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1820, 14)
store_x_eval.shape: (165, 20)
store_x_eval.shape: (176, 20)
store_x_eval.shape: (172, 20)
store_x_eval.shape: (206, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (180, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (165, 20)
Model: "sequential_111"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_223 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_128 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_224 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_129 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_110 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 90ms/step - loss: 33086.4414 - val_loss: 108399.0234 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 90ms/step - loss: 82340.9531 - val_loss: 1208.8224 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 3363.2764 - val_loss: 164.1846 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 643.6526 - val_loss: 15.8108 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 43.5827 - val_loss: 6.1139 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 8.8013 - val_loss: 8.4074 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 6.8056 - val_loss: 2.9637 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 3.8029 - val_loss: 0.8556 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 2.7172 - val_loss: 1.0129 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 2.1389 - val_loss: 0.7229 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 1.9233 - val_loss: 0.3393 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 1.7346 - val_loss: 0.8882 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 1.8277 - val_loss: 0.6586 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 1.5591 - val_loss: 0.7607 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 1.5614 - val_loss: 0.2841 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 1.2353 - val_loss: 0.4948 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 1.2656 - val_loss: 0.2453 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 1.1303 - val_loss: 0.2200 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.9998 - val_loss: 0.3145 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 1.0383 - val_loss: 0.1971 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.9177 - val_loss: 0.9053 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 1.0042 - val_loss: 0.1680 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.8165 - val_loss: 0.2524 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.8053 - val_loss: 0.2095 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.7609 - val_loss: 0.1422 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.6541 - val_loss: 0.8241 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 1.0995 - val_loss: 0.1558 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.5294 - val_loss: 0.1332 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.5698 - val_loss: 0.2147 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.5670 - val_loss: 0.4011 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.6653 - val_loss: 0.1411 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.3983 - val_loss: 0.1111 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.3895 - val_loss: 0.2329 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 90ms/step - loss: 0.3718 - val_loss: 0.1967 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.3645 - val_loss: 0.0973 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.3473 - val_loss: 0.1750 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.3831 - val_loss: 0.3196 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.2604 - val_loss: 0.0983 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.2990 - val_loss: 0.2049 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.2218 - val_loss: 0.0978 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.3939 - val_loss: 0.0411 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.1679 - val_loss: 0.0476 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.2425 - val_loss: 0.0259 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.1983 - val_loss: 0.0232 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.1646 - val_loss: 0.0203 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.2022 - val_loss: 0.0175 Epoch 47/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.0938 - val_loss: 0.0570 Epoch 48/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.1646 - val_loss: 0.1981 Epoch 49/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 88ms/step - loss: 0.1884 - val_loss: 0.1556 Epoch 50/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.1310 - val_loss: 0.0397 Epoch 51/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.0890 - val_loss: 0.0144 Epoch 52/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.1312 - val_loss: 0.0437 Epoch 53/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.0860 - val_loss: 0.0159 Epoch 54/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.2870 - val_loss: 0.0470 Epoch 55/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 90ms/step - loss: 0.1391 - val_loss: 0.0133 Epoch 56/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.0849 - val_loss: 0.0546 Epoch 57/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.1876 - val_loss: 0.0110 Epoch 58/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.0633 - val_loss: 0.0956 Epoch 59/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.0934 - val_loss: 0.0159 Epoch 60/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.0557 - val_loss: 0.0418 Epoch 61/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.1902 - val_loss: 0.0117 Epoch 62/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.0485 - val_loss: 0.0118 Epoch 63/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.1356 - val_loss: 0.0540 Epoch 64/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.1110 - val_loss: 0.0111 Epoch 65/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 9s 89ms/step - loss: 0.1299 - val_loss: 0.0351 Epoch 66/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 0.0467 - val_loss: 0.4760 Epoch 67/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 90ms/step - loss: 0.1946 - val_loss: 0.1004 54/54 ━━━━━━━━━━━━━━━━━━━━ 1s 25ms/step Sample raw predictions (after inverse transform and clipping): [0.6814651 0.12724799 3.3139951 1.7466629 3.230381 ] RMSE = 2.3558202 Validation R-squared for item 1071: 0.1470813751220703 49/49 ━━━━━━━━━━━━━━━━━━━━ 1s 22ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=5.0997514724731445 True y_val range (after inverse transform): min=0.0, max=15.0
-----------------------------------
Current item is 2035
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=14
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.7142857142857142
x_eval_time shape before reshape: (1614, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1894, 14)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (224, 20)
store_x_eval.shape: (217, 20)
store_x_eval.shape: (162, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (180, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (180, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (171, 20)
Model: "sequential_112"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_225 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_130 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_226 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_131 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_111 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 93ms/step - loss: 20651.7949 - val_loss: 796.6826 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 2279.7751 - val_loss: 89.2630 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 312.8403 - val_loss: 4.0923 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 90ms/step - loss: 115.8305 - val_loss: 1.7089 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 68.4937 - val_loss: 3.8763 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 40.6137 - val_loss: 1.0619 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 90ms/step - loss: 26.6705 - val_loss: 3.3223 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 90ms/step - loss: 19.6974 - val_loss: 0.6741 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 16.8798 - val_loss: 0.3387 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 14.2334 - val_loss: 1.6492 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 9.1557 - val_loss: 0.1990 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 8.2573 - val_loss: 0.6116 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 6.7377 - val_loss: 0.5191 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 5.5109 - val_loss: 0.2557 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 4.9593 - val_loss: 0.6626 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 6.0268 - val_loss: 0.0849 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 90ms/step - loss: 5.7536 - val_loss: 1.0590 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 5.7745 - val_loss: 2.1848 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 90ms/step - loss: 3.2487 - val_loss: 0.0950 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 3.7076 - val_loss: 0.0824 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 90ms/step - loss: 2.7866 - val_loss: 1.7842 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 2.5828 - val_loss: 0.0470 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 2.3730 - val_loss: 1.4014 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 2.1942 - val_loss: 0.9276 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 2.3433 - val_loss: 0.0272 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 90ms/step - loss: 3.0175 - val_loss: 0.0877 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 90ms/step - loss: 1.2516 - val_loss: 0.0209 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 1.5412 - val_loss: 1.1831 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 90ms/step - loss: 1.4704 - val_loss: 0.2301 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 1.4559 - val_loss: 0.1861 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 1.0205 - val_loss: 0.2376 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 0.7650 - val_loss: 0.1679 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 1.0846 - val_loss: 5.5067 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 2.6341 - val_loss: 0.1957 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 1.5159 - val_loss: 0.1665 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 0.6524 - val_loss: 1.3828 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 0.7675 - val_loss: 0.7615 52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 26ms/step Sample raw predictions (after inverse transform and clipping): [0.23848294 0. 0.51890236 0. 0.24205278] RMSE = 2.0759268 Validation R-squared for item 2035: -3.9169158935546875 51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=24.821575164794922 True y_val range (after inverse transform): min=0.0, max=10.0
-----------------------------------
Current item is 2370
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=8
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.75
x_eval_time shape before reshape: (1610, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1890, 14)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (175, 20)
store_x_eval.shape: (210, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (183, 20)
Model: "sequential_113"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_227 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_132 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_228 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_133 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_112 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 94ms/step - loss: 17818.8613 - val_loss: 831.9936 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 1641.6783 - val_loss: 59.3500 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 353.9845 - val_loss: 22.0156 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 139.8264 - val_loss: 8.9056 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 68.3212 - val_loss: 2.9254 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 41.4184 - val_loss: 1.1886 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 23.6783 - val_loss: 0.7856 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 15.4516 - val_loss: 0.5027 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 12.4206 - val_loss: 1.3853 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 8.3620 - val_loss: 0.3995 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 6.1768 - val_loss: 0.6855 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 4.5316 - val_loss: 0.5676 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 3.3418 - val_loss: 0.0924 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 3.0338 - val_loss: 0.2661 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 3.5355 - val_loss: 0.0472 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 3.9357 - val_loss: 0.1400 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 3.3209 - val_loss: 0.0939 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 1.9177 - val_loss: 0.0200 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 1.4442 - val_loss: 0.2090 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 1.1646 - val_loss: 0.1208 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 0.7904 - val_loss: 0.0316 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.7120 - val_loss: 0.1957 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.7696 - val_loss: 0.0525 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.6064 - val_loss: 0.3776 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.4754 - val_loss: 0.2592 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.6056 - val_loss: 0.4662 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.5564 - val_loss: 0.0321 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 3.0529 - val_loss: 0.0068 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.9064 - val_loss: 0.0169 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.7567 - val_loss: 0.0976 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.5209 - val_loss: 0.0279 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.6305 - val_loss: 0.0063 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.3765 - val_loss: 0.0087 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.3911 - val_loss: 0.0805 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.3041 - val_loss: 0.3032 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.4000 - val_loss: 0.0117 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.4770 - val_loss: 0.0219 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.3881 - val_loss: 0.0417 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.3245 - val_loss: 0.0522 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.3053 - val_loss: 0.0442 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.2699 - val_loss: 0.2168 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.2499 - val_loss: 0.0592 51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 25ms/step Sample raw predictions (after inverse transform and clipping): [0.08729735 0.03906438 0. 0.42464206 0.65024674] RMSE = 0.6794611 Validation R-squared for item 2370: -0.2652021646499634 51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=1.5332125425338745 True y_val range (after inverse transform): min=0.0, max=6.0
-----------------------------------
Current item is 2444
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=7
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.5714285714285714
x_eval_time shape before reshape: (1639, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1919, 14)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (188, 20)
Model: "sequential_114"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_229 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_134 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_230 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_135 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_113 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 93ms/step - loss: 16581.0645 - val_loss: 190.1826 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 915.2839 - val_loss: 20.9216 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 149.8952 - val_loss: 51.5123 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 278.2110 - val_loss: 16.6080 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 73.6453 - val_loss: 7.2981 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 44.7855 - val_loss: 2.7861 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 32.1076 - val_loss: 1.7387 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 22.3222 - val_loss: 1.1264 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 16.6120 - val_loss: 1.0069 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 11.3983 - val_loss: 1.7574 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 10.1894 - val_loss: 0.5808 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 8.7203 - val_loss: 0.4331 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 7.7899 - val_loss: 0.2702 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 5.7700 - val_loss: 0.9785 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 5.2027 - val_loss: 1.0221 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 4.1083 - val_loss: 0.1879 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 3.4012 - val_loss: 0.1724 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 2.7934 - val_loss: 0.2747 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 2.5976 - val_loss: 0.4959 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 2.3083 - val_loss: 0.1736 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 2.1654 - val_loss: 0.5358 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 1.7956 - val_loss: 0.1877 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 1.7374 - val_loss: 0.3573 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 1.4657 - val_loss: 0.0647 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 1.8538 - val_loss: 0.0267 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 1.6226 - val_loss: 0.0344 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 1.2934 - val_loss: 0.0373 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 1.0960 - val_loss: 0.0308 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.9703 - val_loss: 0.1786 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 0.9248 - val_loss: 0.0728 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.7469 - val_loss: 0.5441 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 91ms/step - loss: 0.8554 - val_loss: 0.1685 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.6894 - val_loss: 0.3928 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.7402 - val_loss: 0.0453 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 92ms/step - loss: 0.5666 - val_loss: 0.3598 51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 25ms/step Sample raw predictions (after inverse transform and clipping): [0.11784206 2.7089045 0.06941345 0.5390756 0.19733861] RMSE = 1.0155368 Validation R-squared for item 2444: -1.353858470916748 52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 23ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=5.2807793617248535 True y_val range (after inverse transform): min=0.0, max=4.0
-----------------------------------
Current item is 1339
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=102
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.5980392156862745
x_eval_time shape before reshape: (1695, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1975, 14)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (207, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (193, 20)
Model: "sequential_115"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_231 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_136 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_232 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_137 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_114 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 96ms/step - loss: 48266.6172 - val_loss: 3645.6582 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 10425.1650 - val_loss: 1423.9637 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 1290.5101 - val_loss: 195.6954 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 563.1725 - val_loss: 58.0825 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 325.3034 - val_loss: 38.7630 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 204.3329 - val_loss: 39.2156 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 162.8071 - val_loss: 7.1148 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 118.0632 - val_loss: 3.7446 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 68.9065 - val_loss: 3.9367 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 51.6228 - val_loss: 3.2063 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 34.6441 - val_loss: 1.5169 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 26.7913 - val_loss: 8.5597 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 20.6993 - val_loss: 2.6844 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 14.7201 - val_loss: 0.9679 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 12.1415 - val_loss: 3.0672 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 9.9589 - val_loss: 5.2705 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 8.2209 - val_loss: 0.8884 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 7.0101 - val_loss: 1.6244 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 7.1893 - val_loss: 3.3497 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 5.6034 - val_loss: 0.5318 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 4.6803 - val_loss: 1.3362 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 4.2341 - val_loss: 5.0382 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 4.5042 - val_loss: 0.4177 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 2.9843 - val_loss: 1.3404 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 3.5364 - val_loss: 0.5387 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 2.6791 - val_loss: 3.1173 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 3.5747 - val_loss: 2.6064 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 2.4079 - val_loss: 0.2929 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 2.6620 - val_loss: 2.0416 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 96ms/step - loss: 2.3798 - val_loss: 0.6102 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 2.4935 - val_loss: 0.3566 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 1.7280 - val_loss: 0.2883 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 1.8638 - val_loss: 0.0983 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 2.5438 - val_loss: 3.3541 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 2.2374 - val_loss: 0.4582 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 2.1242 - val_loss: 0.1501 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 1.8087 - val_loss: 0.3771 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 1.7310 - val_loss: 0.8021 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 1.6239 - val_loss: 0.1434 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 1.4529 - val_loss: 0.6862 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 1.5947 - val_loss: 6.4290 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 2.9599 - val_loss: 0.1073 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 1.9849 - val_loss: 1.1349 52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 26ms/step Sample raw predictions (after inverse transform and clipping): [0. 8.751868 2.5300403 6.2340503 0. ] RMSE = 30.05228 Validation R-squared for item 1339: -12.355067253112793 53/53 ━━━━━━━━━━━━━━━━━━━━ 1s 24ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=302.6733703613281 True y_val range (after inverse transform): min=0.0, max=61.0
-----------------------------------
Current item is 1168
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=18
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.8888888888888888
x_eval_time shape before reshape: (1602, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1882, 14)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (177, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (209, 20)
Model: "sequential_116"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_233 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_138 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_234 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_139 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_115 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 97ms/step - loss: 19570.7969 - val_loss: 11738.8379 Epoch 2/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 7685.4756 - val_loss: 66.3188 Epoch 3/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 405.8290 - val_loss: 31.9284 Epoch 4/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 185.6304 - val_loss: 15.6737 Epoch 5/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 96ms/step - loss: 96.1528 - val_loss: 9.9086 Epoch 6/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 53.6422 - val_loss: 6.2851 Epoch 7/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 35.6351 - val_loss: 4.9108 Epoch 8/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 24.6292 - val_loss: 4.3806 Epoch 9/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 16.7789 - val_loss: 3.3255 Epoch 10/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 12.5379 - val_loss: 2.2650 Epoch 11/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 8.4458 - val_loss: 1.3489 Epoch 12/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 6.6212 - val_loss: 0.7806 Epoch 13/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 6.0906 - val_loss: 0.6395 Epoch 14/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 96ms/step - loss: 4.1261 - val_loss: 0.7412 Epoch 15/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 3.9291 - val_loss: 0.2869 Epoch 16/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 3.1410 - val_loss: 0.1868 Epoch 17/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 2.5537 - val_loss: 0.3546 Epoch 18/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 2.3000 - val_loss: 0.1579 Epoch 19/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 1.6333 - val_loss: 0.1374 Epoch 20/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 1.8197 - val_loss: 0.0987 Epoch 21/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 1.3228 - val_loss: 0.1209 Epoch 22/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 1.1461 - val_loss: 0.0911 Epoch 23/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 1.0987 - val_loss: 0.0590 Epoch 24/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 1.1264 - val_loss: 0.0313 Epoch 25/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.9990 - val_loss: 0.0411 Epoch 26/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.6592 - val_loss: 0.1086 Epoch 27/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.6286 - val_loss: 0.0196 Epoch 28/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.8309 - val_loss: 0.0491 Epoch 29/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.5718 - val_loss: 0.2921 Epoch 30/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 96ms/step - loss: 0.4829 - val_loss: 0.0512 Epoch 31/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.3994 - val_loss: 0.0619 Epoch 32/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.4614 - val_loss: 0.0141 Epoch 33/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.3741 - val_loss: 0.1981 Epoch 34/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.4525 - val_loss: 0.0279 Epoch 35/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.3115 - val_loss: 0.2109 Epoch 36/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.3128 - val_loss: 0.4612 Epoch 37/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.3801 - val_loss: 0.0084 Epoch 38/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 96ms/step - loss: 0.3326 - val_loss: 0.1138 Epoch 39/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.2736 - val_loss: 0.0094 Epoch 40/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.2285 - val_loss: 0.0085 Epoch 41/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.2554 - val_loss: 0.0470 Epoch 42/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 96ms/step - loss: 0.2053 - val_loss: 0.0087 Epoch 43/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.1690 - val_loss: 0.1727 Epoch 44/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 96ms/step - loss: 0.1987 - val_loss: 0.0483 Epoch 45/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 96ms/step - loss: 0.3254 - val_loss: 0.0170 Epoch 46/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.1972 - val_loss: 0.0076 Epoch 47/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.1457 - val_loss: 0.0233 Epoch 48/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.1252 - val_loss: 0.0134 Epoch 49/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.3113 - val_loss: 0.0855 Epoch 50/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.1862 - val_loss: 0.0577 Epoch 51/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.2535 - val_loss: 0.0063 Epoch 52/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.1584 - val_loss: 0.0175 Epoch 53/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.1895 - val_loss: 0.0688 Epoch 54/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.2446 - val_loss: 0.0387 Epoch 55/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.1174 - val_loss: 0.0084 Epoch 56/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.1810 - val_loss: 0.4725 Epoch 57/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 96ms/step - loss: 0.3985 - val_loss: 0.0148 Epoch 58/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.1358 - val_loss: 0.0324 Epoch 59/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.2215 - val_loss: 0.3126 Epoch 60/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 95ms/step - loss: 0.5003 - val_loss: 0.0090 Epoch 61/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 10s 96ms/step - loss: 0.1130 - val_loss: 0.8196 50/50 ━━━━━━━━━━━━━━━━━━━━ 2s 28ms/step Sample raw predictions (after inverse transform and clipping): [0.636791 0.7461896 0.8714721 1.0400041 1.0651879] RMSE = 1.5499761 Validation R-squared for item 1168: -0.16369616985321045 51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 25ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=13.222320556640625 True y_val range (after inverse transform): min=0.0, max=16.0
-----------------------------------
Current item is 2470
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=12
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.75
x_eval_time shape before reshape: (1636, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1916, 14)
store_x_eval.shape: (205, 20)
store_x_eval.shape: (175, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (186, 20)
Model: "sequential_117"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_235 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_140 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_236 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_141 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_116 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 98ms/step - loss: 69991.3125 - val_loss: 15183.8691 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 9160.6084 - val_loss: 449.0584 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 1824.4952 - val_loss: 321.6829 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 96ms/step - loss: 930.6652 - val_loss: 160.3697 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 577.0227 - val_loss: 57.4398 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 361.3781 - val_loss: 31.6635 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 200.3084 - val_loss: 18.2972 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 142.6639 - val_loss: 10.3974 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 89.9479 - val_loss: 12.4704 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 102.5629 - val_loss: 12.0472 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 85.4553 - val_loss: 9.2528 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 96ms/step - loss: 72.8317 - val_loss: 6.5390 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 62.1694 - val_loss: 9.7877 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 48.7639 - val_loss: 5.2090 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 42.7681 - val_loss: 4.4670 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 35.7223 - val_loss: 4.4840 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 30.6255 - val_loss: 3.5240 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 27.7900 - val_loss: 3.1768 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 23.5386 - val_loss: 2.8685 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 20.8099 - val_loss: 2.2918 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 18.4603 - val_loss: 1.9113 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 14.3877 - val_loss: 1.5883 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 12.2412 - val_loss: 4.3001 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 11.7326 - val_loss: 1.7256 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 9.5224 - val_loss: 1.1362 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 8.8460 - val_loss: 1.1270 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 6.7249 - val_loss: 1.3421 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 5.8260 - val_loss: 1.0774 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 96ms/step - loss: 5.1805 - val_loss: 0.4044 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 4.5108 - val_loss: 1.7197 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 3.9182 - val_loss: 0.2620 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 3.1207 - val_loss: 0.9958 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 2.9656 - val_loss: 0.6852 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 3.1922 - val_loss: 0.2326 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 2.4435 - val_loss: 0.1041 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 1.8255 - val_loss: 0.0865 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 1.5131 - val_loss: 0.1211 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 1.7374 - val_loss: 0.1218 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 1.3013 - val_loss: 0.1137 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 1.1680 - val_loss: 0.2849 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.9207 - val_loss: 0.1323 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 1.0218 - val_loss: 0.0798 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 1.0051 - val_loss: 0.0522 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 1.0271 - val_loss: 0.4470 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 1.1497 - val_loss: 0.0535 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 1.0266 - val_loss: 0.6321 Epoch 47/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.8191 - val_loss: 1.0480 Epoch 48/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.8992 - val_loss: 0.0361 Epoch 49/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.6254 - val_loss: 0.0444 Epoch 50/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.9233 - val_loss: 0.5470 Epoch 51/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 1.7033 - val_loss: 0.2173 Epoch 52/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.6473 - val_loss: 1.1025 Epoch 53/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.7226 - val_loss: 0.0278 Epoch 54/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.7034 - val_loss: 0.0788 Epoch 55/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 1.0411 - val_loss: 0.5015 Epoch 56/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.4824 - val_loss: 0.7192 Epoch 57/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.7612 - val_loss: 0.1330 Epoch 58/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.6391 - val_loss: 1.5021 Epoch 59/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.4700 - val_loss: 0.4144 Epoch 60/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 1.0581 - val_loss: 0.1175 Epoch 61/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.3805 - val_loss: 0.3919 Epoch 62/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.6586 - val_loss: 0.8819 Epoch 63/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.9893 - val_loss: 0.0228 Epoch 64/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.3665 - val_loss: 0.0333 Epoch 65/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.4268 - val_loss: 0.0352 Epoch 66/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.5547 - val_loss: 0.0227 Epoch 67/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.3150 - val_loss: 0.7664 Epoch 68/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.7458 - val_loss: 1.2534 Epoch 69/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.5421 - val_loss: 0.8052 Epoch 70/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.8251 - val_loss: 0.1088 Epoch 71/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.2684 - val_loss: 0.0958 Epoch 72/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.9398 - val_loss: 0.0173 Epoch 73/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.3159 - val_loss: 0.3028 Epoch 74/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.4475 - val_loss: 0.6820 Epoch 75/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.7987 - val_loss: 1.3153 Epoch 76/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.3978 - val_loss: 1.8486 Epoch 77/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.5147 - val_loss: 1.1782 Epoch 78/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.8770 - val_loss: 1.2221 Epoch 79/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.3227 - val_loss: 0.0150 Epoch 80/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.2643 - val_loss: 0.0313 Epoch 81/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.2237 - val_loss: 1.3545 Epoch 82/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 1.6647 - val_loss: 0.0166 Epoch 83/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.3633 - val_loss: 0.2935 Epoch 84/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.5912 - val_loss: 0.0324 Epoch 85/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.5718 - val_loss: 0.0349 Epoch 86/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.3092 - val_loss: 0.2277 Epoch 87/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.3683 - val_loss: 0.2744 Epoch 88/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.1930 - val_loss: 0.0483 Epoch 89/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.4699 - val_loss: 0.0159 51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 25ms/step Sample raw predictions (after inverse transform and clipping): [1.8019457 1.6105288 0.9620671 1.0381957 1.0995377] RMSE = 1.5605145 Validation R-squared for item 2470: -0.23828232288360596 52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 28ms/step Predicted y_val range (after inverse transform and clipping): min=0.4473133087158203, max=3.2438201904296875 True y_val range (after inverse transform): min=0.0, max=9.0
-----------------------------------
Current item is 2451
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=9
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.7777777777777777
x_eval_time shape before reshape: (1675, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1955, 14)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (207, 20)
store_x_eval.shape: (207, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (215, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (189, 20)
Model: "sequential_118"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_237 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_142 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_238 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_143 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_117 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 95ms/step - loss: 6962.4102 - val_loss: 97.0596 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 128.7729 - val_loss: 1.3564 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 18.5333 - val_loss: 0.3377 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 9.5818 - val_loss: 0.6846 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 8.1876 - val_loss: 0.6562 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 5.7067 - val_loss: 0.1314 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 3.7670 - val_loss: 0.2134 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 3.1436 - val_loss: 0.1660 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 1.9473 - val_loss: 0.7313 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 1.9534 - val_loss: 0.0603 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 1.5748 - val_loss: 0.1910 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 1.6019 - val_loss: 0.1940 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 1.5782 - val_loss: 0.2581 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 1.4203 - val_loss: 0.1147 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 0.8771 - val_loss: 0.0090 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 1.1411 - val_loss: 0.0728 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 0.9177 - val_loss: 0.0297 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 0.7146 - val_loss: 0.8223 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 0.9136 - val_loss: 0.0105 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 0.4669 - val_loss: 0.2839 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 0.4502 - val_loss: 0.1578 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 1.9200 - val_loss: 0.0308 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 1.0981 - val_loss: 0.0058 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 0.4483 - val_loss: 0.0800 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 0.7885 - val_loss: 0.0184 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 1.7932 - val_loss: 0.1447 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 94ms/step - loss: 0.4078 - val_loss: 0.0105 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 0.2758 - val_loss: 0.1110 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 0.3079 - val_loss: 0.3821 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 0.3680 - val_loss: 0.1388 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 0.4072 - val_loss: 0.3109 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 0.5221 - val_loss: 0.0067 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 93ms/step - loss: 0.1865 - val_loss: 0.5433 49/49 ━━━━━━━━━━━━━━━━━━━━ 1s 27ms/step Sample raw predictions (after inverse transform and clipping): [0.47592464 0.55956143 0.8469352 0.31684068 0.5582867 ] RMSE = 0.7544475 Validation R-squared for item 2451: -0.0637444257736206 53/53 ━━━━━━━━━━━━━━━━━━━━ 1s 24ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=3.654181718826294 True y_val range (after inverse transform): min=0.0, max=7.0
-----------------------------------
Current item is 2092
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=4
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.25
x_eval_time shape before reshape: (1620, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1900, 14)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (174, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (207, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (177, 20)
Model: "sequential_119"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_239 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_144 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_240 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_145 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_118 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 101ms/step - loss: 123705.4062 - val_loss: 53306.6523 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 151662.9531 - val_loss: 883.6946 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 99ms/step - loss: 5679.0498 - val_loss: 372.6973 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 1360.1938 - val_loss: 172.1302 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 901.4240 - val_loss: 42.3644 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 192.0967 - val_loss: 22.3896 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 99ms/step - loss: 97.5519 - val_loss: 16.9523 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 70.5634 - val_loss: 13.4440 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 76.5573 - val_loss: 7.3779 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 45.8745 - val_loss: 3.7886 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 99ms/step - loss: 32.6827 - val_loss: 3.7373 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 99ms/step - loss: 26.4435 - val_loss: 3.2457 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 20.8646 - val_loss: 3.6593 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 20.4365 - val_loss: 2.2921 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 19.2664 - val_loss: 3.0933 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 16.4647 - val_loss: 1.5802 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 13.2117 - val_loss: 1.6984 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 11.8243 - val_loss: 1.0158 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 99ms/step - loss: 10.0006 - val_loss: 1.1602 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 9.9033 - val_loss: 0.9925 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 8.7226 - val_loss: 0.7267 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 7.5389 - val_loss: 0.4587 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 7.7392 - val_loss: 2.4709 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 15.6271 - val_loss: 0.2013 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 4.5824 - val_loss: 0.0217 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 1.6134 - val_loss: 0.1105 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 1.5147 - val_loss: 0.1144 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 99ms/step - loss: 0.9282 - val_loss: 0.0920 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 99ms/step - loss: 0.9057 - val_loss: 0.0394 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 0.6249 - val_loss: 0.1022 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 0.6313 - val_loss: 0.0899 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.5981 - val_loss: 0.0330 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 99ms/step - loss: 0.5022 - val_loss: 0.0472 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.4917 - val_loss: 0.0268 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.4262 - val_loss: 0.0162 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.2988 - val_loss: 0.0127 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 99ms/step - loss: 0.3023 - val_loss: 0.0155 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 99ms/step - loss: 0.2980 - val_loss: 0.0170 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 99ms/step - loss: 0.3025 - val_loss: 0.0192 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 99ms/step - loss: 0.2899 - val_loss: 0.0438 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 0.3153 - val_loss: 0.0279 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 99ms/step - loss: 0.2733 - val_loss: 0.0453 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 99ms/step - loss: 0.2725 - val_loss: 0.0273 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.2695 - val_loss: 0.0399 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 0.2401 - val_loss: 0.0308 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.2271 - val_loss: 0.0455 51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 28ms/step Sample raw predictions (after inverse transform and clipping): [0.18846442 0. 0. 0.17134584 0.11959328] RMSE = 0.3991997 Validation R-squared for item 2092: -0.6738653182983398 51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 26ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=1.1492955684661865 True y_val range (after inverse transform): min=0.0, max=2.0
-----------------------------------
Current item is 2346
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=13
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.2307692307692308
x_eval_time shape before reshape: (1630, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1910, 14)
store_x_eval.shape: (167, 20)
store_x_eval.shape: (210, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (207, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (208, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (162, 20)
Model: "sequential_120"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_241 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_146 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_242 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_147 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_119 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 99ms/step - loss: 4953.7446 - val_loss: 282.4979 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 373.2223 - val_loss: 1.1901 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 10.1258 - val_loss: 0.2302 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 3.6697 - val_loss: 0.0934 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 2.8493 - val_loss: 0.0713 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 2.3891 - val_loss: 0.1119 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 1.6545 - val_loss: 0.1289 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 1.3392 - val_loss: 0.0314 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 1.2406 - val_loss: 0.1396 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.5891 - val_loss: 0.0259 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.5974 - val_loss: 0.0224 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.4662 - val_loss: 0.0195 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.6016 - val_loss: 0.0396 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.3810 - val_loss: 0.0971 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.4921 - val_loss: 0.1130 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.5080 - val_loss: 0.0159 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.4254 - val_loss: 0.0181 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.3240 - val_loss: 0.0575 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.3125 - val_loss: 0.0423 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.1624 - val_loss: 0.0704 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.3577 - val_loss: 0.0146 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.1807 - val_loss: 0.0322 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.2139 - val_loss: 0.0530 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.2352 - val_loss: 0.0202 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.2267 - val_loss: 0.0514 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.1636 - val_loss: 0.0159 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.1858 - val_loss: 0.0163 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.2448 - val_loss: 0.1007 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 97ms/step - loss: 0.1855 - val_loss: 0.0135 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.1156 - val_loss: 0.0169 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.1337 - val_loss: 0.0820 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.1798 - val_loss: 0.0817 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.1606 - val_loss: 0.0714 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.1598 - val_loss: 0.0597 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.1736 - val_loss: 0.0221 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.1004 - val_loss: 0.0319 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.0571 - val_loss: 0.0154 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.0769 - val_loss: 0.0150 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 10s 98ms/step - loss: 0.1112 - val_loss: 0.0172 51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 29ms/step Sample raw predictions (after inverse transform and clipping): [1.2925555 0.6595985 0.8774176 1.0011544 0.7275284] RMSE = 1.4093059 Validation R-squared for item 2346: -0.2229553461074829 51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 26ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=11.396166801452637 True y_val range (after inverse transform): min=0.0, max=16.0
-----------------------------------
Current item is 1385
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=57
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.1403508771929824
x_eval_time shape before reshape: (1682, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1962, 14)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (193, 20)
Model: "sequential_121"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_243 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_148 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_244 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_149 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_120 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 103ms/step - loss: 20203.2441 - val_loss: 466.9173 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 1251.3331 - val_loss: 73.0737 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 158.7083 - val_loss: 18.4454 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 27.2422 - val_loss: 3.0997 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 10.9069 - val_loss: 2.6540 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 5.9858 - val_loss: 1.6445 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 2.1427 - val_loss: 0.7796 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 1.3410 - val_loss: 0.4213 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 1.0756 - val_loss: 0.9193 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 1.0710 - val_loss: 0.3196 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.7817 - val_loss: 0.1159 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.7946 - val_loss: 0.7174 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 1.0420 - val_loss: 0.0397 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.5244 - val_loss: 0.6609 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.6773 - val_loss: 0.3428 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.4954 - val_loss: 0.1682 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.4460 - val_loss: 1.0376 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 0.9936 - val_loss: 0.1772 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.4319 - val_loss: 0.0715 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.3800 - val_loss: 0.1537 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.3044 - val_loss: 0.0297 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.2525 - val_loss: 0.0656 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.3886 - val_loss: 0.0909 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.3597 - val_loss: 0.4006 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.2660 - val_loss: 0.0217 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.2471 - val_loss: 0.0105 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.4025 - val_loss: 1.1108 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.5336 - val_loss: 0.0355 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.1639 - val_loss: 0.1529 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.2582 - val_loss: 0.0854 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.1788 - val_loss: 0.0048 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.2093 - val_loss: 0.0753 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.1089 - val_loss: 0.3914 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.8121 - val_loss: 0.0389 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.4543 - val_loss: 0.0650 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.1021 - val_loss: 0.0150 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.2026 - val_loss: 0.0279 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.0714 - val_loss: 0.0707 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.3425 - val_loss: 0.4742 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.1192 - val_loss: 0.0157 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.1543 - val_loss: 0.2907 51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 30ms/step Sample raw predictions (after inverse transform and clipping): [0. 8.187924 0. 8.680041 5.562109] RMSE = 4.395315 Validation R-squared for item 1385: -0.773018479347229 53/53 ━━━━━━━━━━━━━━━━━━━━ 1s 28ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=12.401473045349121 True y_val range (after inverse transform): min=0.0, max=65.0
-----------------------------------
Current item is 1418
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=82
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.975609756097561
x_eval_time shape before reshape: (1622, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1902, 14)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (210, 20)
store_x_eval.shape: (169, 20)
store_x_eval.shape: (168, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (212, 20)
Model: "sequential_122"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_245 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_150 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_246 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_151 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_121 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 103ms/step - loss: 33253.5156 - val_loss: 753.2000 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 1835.3767 - val_loss: 108.0670 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 370.0514 - val_loss: 33.2398 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 189.1287 - val_loss: 34.0389 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 107.7196 - val_loss: 17.3671 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - loss: 85.1613 - val_loss: 9.4057 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 66.9136 - val_loss: 7.2616 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 47.6285 - val_loss: 13.8233 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 38.1408 - val_loss: 3.2651 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 29.8418 - val_loss: 2.6803 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 21.5605 - val_loss: 1.0225 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 105ms/step - loss: 6.6670 - val_loss: 0.6910 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 2.8295 - val_loss: 0.6176 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 2.0769 - val_loss: 0.7665 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 3.0716 - val_loss: 0.1316 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 1.1946 - val_loss: 0.0699 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 1.0654 - val_loss: 0.1056 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.7720 - val_loss: 0.0883 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.8978 - val_loss: 0.2460 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.8591 - val_loss: 0.1841 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.5533 - val_loss: 0.1297 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.3432 - val_loss: 0.3009 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.3871 - val_loss: 0.2513 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.2799 - val_loss: 0.0464 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.2746 - val_loss: 0.0643 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.2539 - val_loss: 0.1706 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.3925 - val_loss: 0.0672 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.1830 - val_loss: 0.0690 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.5606 - val_loss: 0.3301 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.5521 - val_loss: 0.0163 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.3631 - val_loss: 0.0151 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 1.6377 - val_loss: 0.0345 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.2353 - val_loss: 0.4756 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.4941 - val_loss: 0.0118 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.2196 - val_loss: 0.0804 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.3725 - val_loss: 0.0147 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.1637 - val_loss: 0.1371 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.1303 - val_loss: 0.0229 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - loss: 0.3142 - val_loss: 0.0878 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 7.9537 - val_loss: 0.0367 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.3793 - val_loss: 0.0206 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.1645 - val_loss: 0.0151 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.1468 - val_loss: 0.0990 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.0843 - val_loss: 0.0357 51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 30ms/step Sample raw predictions (after inverse transform and clipping): [12.608353 16.859123 10.918046 1.6098084 1.5930306] RMSE = 9.554832 Validation R-squared for item 1418: -0.34960055351257324 51/51 ━━━━━━━━━━━━━━━━━━━━ 1s 27ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=28.063865661621094 True y_val range (after inverse transform): min=0.0, max=80.0
-----------------------------------
Current item is 2218
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=4
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.0
x_eval_time shape before reshape: (1674, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1954, 14)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (217, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (162, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (218, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (191, 20)
Model: "sequential_123"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_247 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_152 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_248 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_153 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_122 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 104ms/step - loss: 26117.4531 - val_loss: 1021.0290 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 2339.4175 - val_loss: 100.6271 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 493.4540 - val_loss: 55.7553 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 168.6380 - val_loss: 27.8086 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 94.5537 - val_loss: 8.1227 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 44.8432 - val_loss: 8.1789 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 20.2939 - val_loss: 5.6488 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 12.1928 - val_loss: 4.0152 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 8.2380 - val_loss: 5.2129 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 6.1950 - val_loss: 2.0753 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 4.1176 - val_loss: 2.6222 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 3.0636 - val_loss: 0.3547 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 1.6137 - val_loss: 2.8439 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 1.2795 - val_loss: 1.2249 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.9136 - val_loss: 1.5028 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.6644 - val_loss: 0.5135 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.5498 - val_loss: 0.3088 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.4956 - val_loss: 0.4442 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.4006 - val_loss: 0.2699 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.4868 - val_loss: 0.2363 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.3439 - val_loss: 0.7047 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.3556 - val_loss: 0.1047 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.2714 - val_loss: 0.0146 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.2201 - val_loss: 1.1833 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.6891 - val_loss: 0.0188 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.2898 - val_loss: 0.1904 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.3585 - val_loss: 0.9802 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.3860 - val_loss: 0.1198 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.2565 - val_loss: 0.0498 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.3268 - val_loss: 0.0122 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.3958 - val_loss: 0.0448 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.1953 - val_loss: 3.1627 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.6825 - val_loss: 1.7181 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.5474 - val_loss: 0.0481 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.2810 - val_loss: 0.0160 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.3253 - val_loss: 0.2027 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.2654 - val_loss: 0.1589 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.2950 - val_loss: 0.8831 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.3162 - val_loss: 0.2269 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.3514 - val_loss: 0.2898 52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 30ms/step Sample raw predictions (after inverse transform and clipping): [0.46083382 0.47313693 0.41631088 0.40143654 0.4207708 ] RMSE = 0.47188592 Validation R-squared for item 2218: -0.48597514629364014 53/53 ━━━━━━━━━━━━━━━━━━━━ 1s 27ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=0.8005332350730896 True y_val range (after inverse transform): min=0.0, max=4.0
-----------------------------------
Current item is 1372
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=24
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.75
x_eval_time shape before reshape: (1691, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1971, 14)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (206, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (174, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (212, 20)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (190, 20)
Model: "sequential_124"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_249 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_154 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_250 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_155 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_123 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 12s 104ms/step - loss: 5724.9556 - val_loss: 31.0461 Epoch 2/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 78.7968 - val_loss: 4.7368 Epoch 3/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 18.5161 - val_loss: 2.0129 Epoch 4/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 7.3736 - val_loss: 0.2345 Epoch 5/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 2.2392 - val_loss: 0.0856 Epoch 6/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 1.2214 - val_loss: 0.0450 Epoch 7/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - loss: 0.6485 - val_loss: 0.0197 Epoch 8/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - loss: 0.4221 - val_loss: 0.0330 Epoch 9/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.4275 - val_loss: 0.0372 Epoch 10/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.2816 - val_loss: 0.0394 Epoch 11/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.1987 - val_loss: 0.0046 Epoch 12/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.1332 - val_loss: 0.0110 Epoch 13/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.1902 - val_loss: 0.0427 Epoch 14/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 0.3585 - val_loss: 0.0482 Epoch 15/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.1996 - val_loss: 0.0031 Epoch 16/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.0895 - val_loss: 0.0028 Epoch 17/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.0860 - val_loss: 0.0040 Epoch 18/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.0475 - val_loss: 0.0474 Epoch 19/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.1608 - val_loss: 0.0364 Epoch 20/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.1435 - val_loss: 0.0269 Epoch 21/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.0264 - val_loss: 0.0271 Epoch 22/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.0416 - val_loss: 0.0081 Epoch 23/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.0479 - val_loss: 0.0313 Epoch 24/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.0604 - val_loss: 0.0035 Epoch 25/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.0315 - val_loss: 0.0212 Epoch 26/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 11s 102ms/step - loss: 0.0384 - val_loss: 0.0121 53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 31ms/step Sample raw predictions (after inverse transform and clipping): [1.6629157 1.7624677 1.6999266 1.8415756 1.8864608] RMSE = 1.9634923 Validation R-squared for item 1372: -0.02215099334716797 53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 28ms/step Predicted y_val range (after inverse transform and clipping): min=0.02439943514764309, max=3.868675708770752 True y_val range (after inverse transform): min=0.0, max=18.0
-----------------------------------
Current item is 2091
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=5
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.6000000000000001
x_eval_time shape before reshape: (1648, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1928, 14)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (192, 20)
Model: "sequential_125"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_251 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_156 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_252 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_157 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_124 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 101ms/step - loss: 2399.5427 - val_loss: 5.9515 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 33.9086 - val_loss: 3.0762 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 9.5099 - val_loss: 0.2979 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 4.3145 - val_loss: 1.2266 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 2.8398 - val_loss: 0.1496 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 1.7588 - val_loss: 0.0381 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 101ms/step - loss: 1.1148 - val_loss: 0.0973 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 1.0644 - val_loss: 0.4346 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.6968 - val_loss: 0.0190 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 0.4938 - val_loss: 0.0293 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 0.4478 - val_loss: 0.0765 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 0.3174 - val_loss: 0.1364 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 0.4236 - val_loss: 0.7585 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.2486 - val_loss: 0.8574 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 0.3788 - val_loss: 0.1644 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.2394 - val_loss: 0.6668 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 0.2741 - val_loss: 0.1288 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 99ms/step - loss: 0.1347 - val_loss: 0.0957 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 100ms/step - loss: 0.1741 - val_loss: 0.0660 50/50 ━━━━━━━━━━━━━━━━━━━━ 2s 31ms/step Sample raw predictions (after inverse transform and clipping): [0.22082053 0. 0.37015793 0. 0.02117402] RMSE = 0.615649 Validation R-squared for item 2091: -2.034963369369507 52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 27ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=2.7173492908477783 True y_val range (after inverse transform): min=0.0, max=3.0
-----------------------------------
Current item is 1321
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=48
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.47916666666666663
x_eval_time shape before reshape: (1678, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1958, 14)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (173, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (207, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (214, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (196, 20)
Model: "sequential_126"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_253 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_158 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_254 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_159 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_125 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 107ms/step - loss: 26642.7246 - val_loss: 10016.4141 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 8470.3135 - val_loss: 285.4241 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 792.8690 - val_loss: 52.5792 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 329.8942 - val_loss: 59.1814 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 95.6770 - val_loss: 13.1222 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - loss: 34.2644 - val_loss: 3.8331 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 13.5210 - val_loss: 2.0641 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 9.1096 - val_loss: 3.1732 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 7.3223 - val_loss: 1.3718 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 5.8122 - val_loss: 1.6456 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 105ms/step - loss: 4.9873 - val_loss: 1.5436 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 4.1322 - val_loss: 0.5961 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 105ms/step - loss: 3.9847 - val_loss: 1.6482 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 108ms/step - loss: 3.5734 - val_loss: 0.4655 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 105ms/step - loss: 3.2045 - val_loss: 1.3978 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 2.3909 - val_loss: 0.2719 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 105ms/step - loss: 2.1701 - val_loss: 0.3832 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 2.0454 - val_loss: 0.1440 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 2.3097 - val_loss: 0.3282 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 2.1508 - val_loss: 0.4985 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 105ms/step - loss: 18.8430 - val_loss: 0.0964 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 105ms/step - loss: 1.6706 - val_loss: 0.1185 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 105ms/step - loss: 1.3081 - val_loss: 0.2193 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 105ms/step - loss: 1.5176 - val_loss: 0.0724 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 1.2519 - val_loss: 0.3847 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 1.2598 - val_loss: 0.2339 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 1.0821 - val_loss: 2.4954 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 2.2943 - val_loss: 0.2131 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 1.0847 - val_loss: 0.1608 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 1.0928 - val_loss: 0.2702 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 0.9180 - val_loss: 0.0316 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 0.8756 - val_loss: 0.4019 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 0.8001 - val_loss: 0.0905 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 105ms/step - loss: 0.7809 - val_loss: 0.4150 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 105ms/step - loss: 0.8066 - val_loss: 0.3795 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 0.7435 - val_loss: 0.0259 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 0.9482 - val_loss: 0.8153 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 0.7181 - val_loss: 0.7545 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 105ms/step - loss: 0.7100 - val_loss: 0.0172 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 105ms/step - loss: 0.5606 - val_loss: 0.0126 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 0.6853 - val_loss: 1.0255 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 1.0428 - val_loss: 0.2581 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 105ms/step - loss: 0.5939 - val_loss: 0.0819 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - loss: 0.6007 - val_loss: 0.0249 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - loss: 0.6249 - val_loss: 0.0767 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - loss: 0.5363 - val_loss: 0.2901 Epoch 47/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - loss: 0.6112 - val_loss: 0.0102 Epoch 48/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 105ms/step - loss: 0.5338 - val_loss: 0.0613 Epoch 49/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - loss: 0.6167 - val_loss: 0.1932 Epoch 50/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - loss: 0.6320 - val_loss: 0.1256 Epoch 51/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - loss: 0.4279 - val_loss: 0.0416 Epoch 52/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - loss: 0.7995 - val_loss: 0.0445 Epoch 53/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 105ms/step - loss: 0.4439 - val_loss: 0.1581 Epoch 54/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - loss: 0.3824 - val_loss: 0.0527 Epoch 55/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - loss: 0.3781 - val_loss: 0.0122 Epoch 56/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 104ms/step - loss: 0.3610 - val_loss: 0.1865 Epoch 57/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 103ms/step - loss: 0.3452 - val_loss: 0.0373 50/50 ━━━━━━━━━━━━━━━━━━━━ 2s 31ms/step Sample raw predictions (after inverse transform and clipping): [0.2769277 0. 2.7946901 7.0255384 2.791136 ] RMSE = 4.542462 Validation R-squared for item 1321: -2.18985652923584 53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 29ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=24.288002014160156 True y_val range (after inverse transform): min=0.0, max=23.0
-----------------------------------
Current item is 1060
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=4
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.5
x_eval_time shape before reshape: (1638, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1918, 14)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (178, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (208, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (180, 20)
store_x_eval.shape: (191, 20)
Model: "sequential_127"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_255 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_160 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_256 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_161 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_126 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 107ms/step - loss: 42605.7500 - val_loss: 81669.3984 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 104442.1250 - val_loss: 371.5465 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 5457.7129 - val_loss: 186.1731 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 3335.4199 - val_loss: 309.5009 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 839.9453 - val_loss: 181.3387 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 521.3579 - val_loss: 70.6890 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 336.7665 - val_loss: 59.7723 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 108ms/step - loss: 255.8872 - val_loss: 36.1934 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 108ms/step - loss: 174.8770 - val_loss: 16.5178 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 117.3841 - val_loss: 16.3701 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 91.1494 - val_loss: 13.6119 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 79.5953 - val_loss: 8.6184 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 67.7404 - val_loss: 20.8481 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 59.4776 - val_loss: 5.3356 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 48.1804 - val_loss: 4.9601 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 44.5957 - val_loss: 6.1596 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 38.6015 - val_loss: 8.5011 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 34.7414 - val_loss: 7.7797 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 29.8312 - val_loss: 4.0536 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 27.2458 - val_loss: 5.0275 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 23.1728 - val_loss: 3.0905 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 19.2566 - val_loss: 3.3138 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 18.3725 - val_loss: 2.7668 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 16.1136 - val_loss: 4.3245 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 15.0155 - val_loss: 2.3340 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 15.2844 - val_loss: 3.6990 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 12.7913 - val_loss: 2.4430 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 13.7159 - val_loss: 1.5232 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 10.6155 - val_loss: 1.3569 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 9.2333 - val_loss: 1.3992 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 9.1264 - val_loss: 1.4151 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 7.7125 - val_loss: 1.1428 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 6.6515 - val_loss: 1.2609 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 6.6926 - val_loss: 1.0533 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 6.1642 - val_loss: 1.0142 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 5.7694 - val_loss: 0.8133 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 7.6656 - val_loss: 1.9801 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 5.1468 - val_loss: 0.6259 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 3.9052 - val_loss: 1.6552 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 3.5883 - val_loss: 0.8517 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 3.6176 - val_loss: 0.4544 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 3.2406 - val_loss: 1.1138 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 2.7879 - val_loss: 3.5464 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 3.3240 - val_loss: 3.2578 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 3.4700 - val_loss: 0.4072 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 2.5223 - val_loss: 0.1964 Epoch 47/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 1.7702 - val_loss: 0.3169 Epoch 48/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 1.8534 - val_loss: 0.2098 Epoch 49/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 1.5688 - val_loss: 0.5105 Epoch 50/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 1.5187 - val_loss: 0.1798 Epoch 51/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 1.1653 - val_loss: 0.1694 Epoch 52/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 2.9463 - val_loss: 0.3497 Epoch 53/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 0.9548 - val_loss: 0.6854 Epoch 54/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 0.8543 - val_loss: 0.7225 Epoch 55/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 0.9625 - val_loss: 0.1476 Epoch 56/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 0.6544 - val_loss: 0.1264 Epoch 57/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 0.6192 - val_loss: 0.0367 Epoch 58/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 0.5377 - val_loss: 0.0290 Epoch 59/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 0.4534 - val_loss: 0.0201 Epoch 60/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 0.5571 - val_loss: 0.0922 Epoch 61/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 0.6980 - val_loss: 0.1432 Epoch 62/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 0.6835 - val_loss: 1.1766 Epoch 63/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 0.6415 - val_loss: 1.7632 Epoch 64/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 3.3025 - val_loss: 0.2744 Epoch 65/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 0.8255 - val_loss: 0.0910 Epoch 66/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 0.3794 - val_loss: 0.0420 Epoch 67/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 0.5802 - val_loss: 0.3924 Epoch 68/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 0.6328 - val_loss: 0.5087 Epoch 69/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 1.2022 - val_loss: 0.0140 Epoch 70/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 0.4148 - val_loss: 0.0119 Epoch 71/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 2.4880 - val_loss: 0.6624 Epoch 72/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 0.6289 - val_loss: 0.0709 Epoch 73/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 4.8814 - val_loss: 0.1735 Epoch 74/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 0.2851 - val_loss: 0.1752 Epoch 75/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 1.7202 - val_loss: 0.0378 Epoch 76/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 0.5379 - val_loss: 12.3370 Epoch 77/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 4.2169 - val_loss: 0.0927 Epoch 78/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 0.2617 - val_loss: 0.8169 Epoch 79/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 106ms/step - loss: 1.3088 - val_loss: 0.1503 Epoch 80/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 107ms/step - loss: 0.1835 - val_loss: 7.5350 51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 31ms/step Sample raw predictions (after inverse transform and clipping): [0. 0. 0. 0. 0.] RMSE = 0.24507123 Validation R-squared for item 1060: -0.0482640266418457 52/52 ━━━━━━━━━━━━━━━━━━━━ 1s 28ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=0.2094658762216568 True y_val range (after inverse transform): min=0.0, max=2.0
-----------------------------------
Current item is 1465
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=94
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.5106382978723404
x_eval_time shape before reshape: (1591, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1871, 14)
store_x_eval.shape: (208, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (170, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (165, 20)
Model: "sequential_128"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_257 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_162 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_258 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_163 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_127 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 111ms/step - loss: 1777.5757 - val_loss: 99.6648 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 305.4733 - val_loss: 0.5545 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 2.7107 - val_loss: 0.1199 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 1.1199 - val_loss: 0.0443 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.5669 - val_loss: 0.0173 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 0.3828 - val_loss: 0.1360 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.5657 - val_loss: 0.1505 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.3481 - val_loss: 0.0112 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.2822 - val_loss: 0.0094 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.2367 - val_loss: 0.0058 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1321 - val_loss: 0.0633 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1493 - val_loss: 0.0219 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1407 - val_loss: 0.0672 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1520 - val_loss: 0.0069 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1023 - val_loss: 0.0191 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0677 - val_loss: 0.0110 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 108ms/step - loss: 0.1329 - val_loss: 0.0109 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 108ms/step - loss: 0.0860 - val_loss: 0.0097 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0839 - val_loss: 0.3072 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1074 - val_loss: 0.0056 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0694 - val_loss: 0.0289 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0771 - val_loss: 0.0245 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0533 - val_loss: 0.1616 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.0979 - val_loss: 0.0115 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0362 - val_loss: 0.0468 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0472 - val_loss: 0.4819 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1189 - val_loss: 0.0257 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 108ms/step - loss: 0.0321 - val_loss: 0.1822 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0733 - val_loss: 0.0054 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0745 - val_loss: 0.0091 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0334 - val_loss: 0.0685 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1372 - val_loss: 0.0337 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0386 - val_loss: 0.0098 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0320 - val_loss: 0.0078 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0240 - val_loss: 0.0047 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.0174 - val_loss: 0.0077 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0738 - val_loss: 0.0185 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.0551 - val_loss: 0.0043 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0166 - val_loss: 0.0295 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0237 - val_loss: 0.0232 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0547 - val_loss: 0.0044 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0213 - val_loss: 0.0120 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0288 - val_loss: 0.0047 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0205 - val_loss: 0.0044 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0132 - val_loss: 0.0047 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0207 - val_loss: 0.0125 Epoch 47/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0333 - val_loss: 0.0044 Epoch 48/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 11s 108ms/step - loss: 0.0250 - val_loss: 0.0414 53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 32ms/step Sample raw predictions (after inverse transform and clipping): [4.6931977 3.9251904 5.151284 2.185066 3.0338645] RMSE = 7.6439342 Validation R-squared for item 1465: -0.011783719062805176 50/50 ━━━━━━━━━━━━━━━━━━━━ 1s 28ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=37.372379302978516 True y_val range (after inverse transform): min=0.0, max=48.0
-----------------------------------
Current item is 1291
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=14
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.6428571428571428
x_eval_time shape before reshape: (1659, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1939, 14)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (219, 20)
store_x_eval.shape: (181, 20)
Model: "sequential_129"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_259 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_164 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_260 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_165 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_128 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 112ms/step - loss: 16102.6553 - val_loss: 811.3441 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 1299.3628 - val_loss: 78.9109 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 193.3227 - val_loss: 49.1470 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 98.2203 - val_loss: 24.8354 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 56.0757 - val_loss: 10.2144 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 34.4090 - val_loss: 9.1613 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 20.9415 - val_loss: 4.4702 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 13.7581 - val_loss: 1.1526 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 9.2829 - val_loss: 1.9566 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 6.3890 - val_loss: 0.3736 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 4.6124 - val_loss: 0.7061 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 2.9069 - val_loss: 0.1715 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 1.8970 - val_loss: 0.3491 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 1.3501 - val_loss: 0.1987 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.9858 - val_loss: 0.0990 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.6534 - val_loss: 0.1171 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.6489 - val_loss: 0.0614 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.4813 - val_loss: 0.0449 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.5071 - val_loss: 0.1137 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.3763 - val_loss: 0.0224 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.2938 - val_loss: 0.0301 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.2196 - val_loss: 0.0314 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.2054 - val_loss: 0.1169 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.2438 - val_loss: 0.1766 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1914 - val_loss: 0.1210 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1486 - val_loss: 0.4602 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.2205 - val_loss: 0.1158 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1742 - val_loss: 0.0127 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1574 - val_loss: 0.0915 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1574 - val_loss: 0.0125 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1207 - val_loss: 0.1246 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1836 - val_loss: 0.0347 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1266 - val_loss: 0.0128 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.1406 - val_loss: 0.0345 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.3270 - val_loss: 0.0662 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1801 - val_loss: 0.5065 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.3720 - val_loss: 0.0640 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1018 - val_loss: 0.0326 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.3387 - val_loss: 0.0108 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.2062 - val_loss: 0.0128 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1500 - val_loss: 0.0094 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.4160 - val_loss: 0.2054 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.2423 - val_loss: 0.2453 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1943 - val_loss: 1.2376 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.3358 - val_loss: 0.0751 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.0959 - val_loss: 0.0093 Epoch 47/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.4395 - val_loss: 0.1239 Epoch 48/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0910 - val_loss: 0.0096 Epoch 49/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.0762 - val_loss: 0.0354 Epoch 50/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.4144 - val_loss: 0.0601 Epoch 51/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.2033 - val_loss: 0.1264 Epoch 52/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1208 - val_loss: 0.0239 Epoch 53/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.2875 - val_loss: 0.0564 Epoch 54/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.3277 - val_loss: 0.0422 Epoch 55/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.4116 - val_loss: 5.4515 Epoch 56/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 169.1634 - val_loss: 0.5198 52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 31ms/step Sample raw predictions (after inverse transform and clipping): [1.2624756 1.1463574 1.2206677 1.1041883 1.7262777] RMSE = 1.372813 Validation R-squared for item 1291: -0.08289897441864014 52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 29ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=3.798078775405884 True y_val range (after inverse transform): min=0.0, max=9.0
-----------------------------------
Current item is 1424
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=45
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.35555555555555557
x_eval_time shape before reshape: (1692, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1972, 14)
store_x_eval.shape: (213, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (210, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (176, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (195, 20)
Model: "sequential_130"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_261 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_166 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_262 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_167 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_129 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 112ms/step - loss: 16555.3027 - val_loss: 1055.9011 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 1088.1816 - val_loss: 114.7415 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 134.3547 - val_loss: 71.1422 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 78.3459 - val_loss: 42.7679 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 48.7246 - val_loss: 21.2582 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 32.2987 - val_loss: 15.8533 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 23.6187 - val_loss: 7.2489 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 16.4161 - val_loss: 6.7680 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 13.2771 - val_loss: 2.7323 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 10.8621 - val_loss: 4.1661 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 8.6918 - val_loss: 1.5530 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 7.0684 - val_loss: 0.4237 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 6.1815 - val_loss: 0.7660 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 4.4506 - val_loss: 0.3226 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 4.2689 - val_loss: 0.6778 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 3.2634 - val_loss: 0.5190 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 2.7713 - val_loss: 0.0771 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 2.0557 - val_loss: 0.0743 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 1.3968 - val_loss: 0.0710 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 0.9309 - val_loss: 0.0274 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.7333 - val_loss: 0.0603 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.7850 - val_loss: 0.0163 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.5288 - val_loss: 0.0265 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.5180 - val_loss: 0.0051 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.3653 - val_loss: 0.0093 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.3243 - val_loss: 0.0227 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 1.5620 - val_loss: 0.0168 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.2769 - val_loss: 0.0104 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.2287 - val_loss: 0.0409 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.2273 - val_loss: 0.0105 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.1986 - val_loss: 9.6365e-04 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.1576 - val_loss: 0.0119 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.1496 - val_loss: 0.0067 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.1849 - val_loss: 0.0024 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.1156 - val_loss: 0.0989 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.1101 - val_loss: 0.0152 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.0565 - val_loss: 0.0326 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.0751 - val_loss: 0.0413 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.1574 - val_loss: 0.0028 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.0779 - val_loss: 0.2170 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.1020 - val_loss: 0.0213 50/50 ━━━━━━━━━━━━━━━━━━━━ 2s 33ms/step Sample raw predictions (after inverse transform and clipping): [1.2860903 1.7684357 1.3122737 1.6934654 1.9910855] RMSE = 1.3479356 Validation R-squared for item 1424: -0.3858039379119873 53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 30ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=5.76594352722168 True y_val range (after inverse transform): min=0.0, max=16.0
-----------------------------------
Current item is 2504
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=10
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.0
x_eval_time shape before reshape: (1628, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1908, 14)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (205, 20)
store_x_eval.shape: (151, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (167, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (222, 20)
Model: "sequential_131"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_263 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_168 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_264 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_169 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_130 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 13s 112ms/step - loss: 49501.1250 - val_loss: 1457.8612 Epoch 2/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 6897.5859 - val_loss: 291.0981 Epoch 3/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 1654.0198 - val_loss: 91.3291 Epoch 4/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 591.3803 - val_loss: 53.7344 Epoch 5/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 392.3601 - val_loss: 36.7696 Epoch 6/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 237.3869 - val_loss: 11.9364 Epoch 7/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 151.2194 - val_loss: 10.7370 Epoch 8/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 102.2039 - val_loss: 6.6637 Epoch 9/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 80.5547 - val_loss: 3.7511 Epoch 10/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 55.0267 - val_loss: 3.8237 Epoch 11/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 49.1561 - val_loss: 2.4406 Epoch 12/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 37.4136 - val_loss: 2.5521 Epoch 13/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 28.9135 - val_loss: 1.9241 Epoch 14/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 21.1801 - val_loss: 1.6021 Epoch 15/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 16.8246 - val_loss: 1.2353 Epoch 16/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 12.6793 - val_loss: 1.4521 Epoch 17/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 10.1088 - val_loss: 0.6198 Epoch 18/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 7.6257 - val_loss: 0.3926 Epoch 19/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 6.1579 - val_loss: 0.3628 Epoch 20/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 4.2015 - val_loss: 0.6605 Epoch 21/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 4.1416 - val_loss: 0.3572 Epoch 22/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 4.6031 - val_loss: 0.1419 Epoch 23/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 3.4177 - val_loss: 0.1585 Epoch 24/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 2.9606 - val_loss: 0.1034 Epoch 25/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 2.5789 - val_loss: 0.0516 Epoch 26/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 1.7069 - val_loss: 0.0883 Epoch 27/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 1.2539 - val_loss: 0.0733 Epoch 28/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 1.3308 - val_loss: 0.0928 Epoch 29/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 1.0886 - val_loss: 0.0744 Epoch 30/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.7453 - val_loss: 0.0213 Epoch 31/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 1.5673 - val_loss: 0.0191 Epoch 32/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 1.0726 - val_loss: 0.0086 Epoch 33/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 1.0207 - val_loss: 0.0876 Epoch 34/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.9975 - val_loss: 0.0467 Epoch 35/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 1.0030 - val_loss: 0.0372 Epoch 36/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 1.0663 - val_loss: 0.0130 Epoch 37/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.6083 - val_loss: 0.0068 Epoch 38/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.6745 - val_loss: 0.0155 Epoch 39/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.6257 - val_loss: 0.0070 Epoch 40/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.5369 - val_loss: 0.0155 Epoch 41/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.6417 - val_loss: 0.0076 Epoch 42/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.5153 - val_loss: 0.0069 Epoch 43/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.4003 - val_loss: 0.0243 Epoch 44/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.7704 - val_loss: 0.0148 Epoch 45/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 1.1722 - val_loss: 0.0138 Epoch 46/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.2443 - val_loss: 0.0164 Epoch 47/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.2777 - val_loss: 0.0026 Epoch 48/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 0.3870 - val_loss: 0.0061 Epoch 49/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.2286 - val_loss: 0.0060 Epoch 50/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.2514 - val_loss: 0.0066 Epoch 51/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1248 - val_loss: 0.0324 Epoch 52/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.2122 - val_loss: 0.0109 Epoch 53/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1281 - val_loss: 0.0040 Epoch 54/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.2736 - val_loss: 0.0026 Epoch 55/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.2011 - val_loss: 0.0202 Epoch 56/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.2423 - val_loss: 1.2271 Epoch 57/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.4234 - val_loss: 0.0437 50/50 ━━━━━━━━━━━━━━━━━━━━ 2s 32ms/step Sample raw predictions (after inverse transform and clipping): [0. 0.13709041 0.03325705 0. 0.0111235 ] RMSE = 0.4554332 Validation R-squared for item 2504: -0.35741889476776123 51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 29ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=7.019077301025391 True y_val range (after inverse transform): min=0.0, max=10.0
-----------------------------------
Current item is 2010
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=10
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.4
x_eval_time shape before reshape: (1589, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1869, 14)
store_x_eval.shape: (154, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (160, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (177, 20)
store_x_eval.shape: (200, 20)
Model: "sequential_132"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_265 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_170 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_266 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_171 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_131 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 111ms/step - loss: 22493.8184 - val_loss: 2234.3696 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 4772.0278 - val_loss: 62.7961 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 290.8988 - val_loss: 30.0902 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 88.4296 - val_loss: 10.1840 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 38.5198 - val_loss: 2.4335 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 21.9122 - val_loss: 2.6231 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 13.1744 - val_loss: 1.1795 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 8.3515 - val_loss: 0.7782 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 6.0252 - val_loss: 2.7243 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 4.9337 - val_loss: 0.5583 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 2.0704 - val_loss: 0.2174 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.8485 - val_loss: 0.1420 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.8534 - val_loss: 0.0387 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.6515 - val_loss: 0.1992 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.7420 - val_loss: 0.0768 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.9445 - val_loss: 0.1881 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.4814 - val_loss: 0.0428 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.5088 - val_loss: 0.4363 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.4450 - val_loss: 0.1149 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.4149 - val_loss: 0.0155 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.2994 - val_loss: 0.1041 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.3306 - val_loss: 0.1352 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.2720 - val_loss: 0.0246 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.3184 - val_loss: 0.0354 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 1.1417 - val_loss: 0.0106 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.2126 - val_loss: 0.1318 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1487 - val_loss: 0.0758 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1600 - val_loss: 0.0938 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.2023 - val_loss: 0.0084 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1676 - val_loss: 0.0346 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1691 - val_loss: 0.0316 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1016 - val_loss: 0.1894 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1469 - val_loss: 0.0574 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.2326 - val_loss: 0.0911 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.7799 - val_loss: 0.0539 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1186 - val_loss: 0.0562 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.0847 - val_loss: 0.0044 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.0928 - val_loss: 0.0526 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0996 - val_loss: 0.0162 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0788 - val_loss: 0.0464 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1124 - val_loss: 0.0337 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0877 - val_loss: 0.1132 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1387 - val_loss: 0.0153 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1028 - val_loss: 0.0384 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0810 - val_loss: 0.0025 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0890 - val_loss: 0.0479 Epoch 47/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1049 - val_loss: 0.0069 Epoch 48/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.1767 - val_loss: 0.0395 Epoch 49/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.0618 - val_loss: 0.0037 Epoch 50/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.0760 - val_loss: 0.0392 Epoch 51/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.0684 - val_loss: 0.0119 Epoch 52/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0582 - val_loss: 0.1018 Epoch 53/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 3.7670 - val_loss: 1.2228 Epoch 54/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.6029 - val_loss: 0.0018 Epoch 55/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0289 - val_loss: 0.0104 Epoch 56/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0276 - val_loss: 0.0015 Epoch 57/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0165 - val_loss: 0.0046 Epoch 58/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0373 - val_loss: 0.0120 Epoch 59/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0164 - val_loss: 0.0071 Epoch 60/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0394 - val_loss: 0.0048 Epoch 61/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0155 - val_loss: 0.0100 Epoch 62/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0162 - val_loss: 0.0026 Epoch 63/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0115 - val_loss: 0.0012 Epoch 64/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0144 - val_loss: 0.0083 Epoch 65/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0153 - val_loss: 0.0060 Epoch 66/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 0.0402 - val_loss: 0.0026 Epoch 67/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0231 - val_loss: 0.0182 Epoch 68/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0238 - val_loss: 0.0013 Epoch 69/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1408 - val_loss: 0.1863 Epoch 70/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.1251 - val_loss: 0.0053 Epoch 71/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0123 - val_loss: 0.0013 Epoch 72/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0112 - val_loss: 0.0049 Epoch 73/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 109ms/step - loss: 0.0133 - val_loss: 0.0110 53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 32ms/step Sample raw predictions (after inverse transform and clipping): [0.31749117 0.18959306 0.06241129 0.07039153 0.14451277] RMSE = 0.39962238 Validation R-squared for item 2010: -0.12027716636657715 50/50 ━━━━━━━━━━━━━━━━━━━━ 1s 29ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=0.7126240730285645 True y_val range (after inverse transform): min=0.0, max=4.0
-----------------------------------
Current item is 1445
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=27
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.37037037037037035
x_eval_time shape before reshape: (1673, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1953, 14)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (175, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (208, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (218, 20)
Model: "sequential_133"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_267 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_172 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_268 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_173 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_132 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 116ms/step - loss: 9759.8643 - val_loss: 42.2506 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 377.8294 - val_loss: 18.4006 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 113.0265 - val_loss: 5.9370 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 20.4474 - val_loss: 0.6966 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 5.0812 - val_loss: 0.2164 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 3.0266 - val_loss: 0.6688 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 2.6114 - val_loss: 0.4564 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 1.3599 - val_loss: 0.1441 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.9022 - val_loss: 0.0241 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.7821 - val_loss: 0.0576 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.7841 - val_loss: 0.4943 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.4081 - val_loss: 0.3008 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.5914 - val_loss: 0.0496 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.3676 - val_loss: 0.0085 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.2714 - val_loss: 0.2167 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.3167 - val_loss: 0.1324 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.2388 - val_loss: 0.3584 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.3686 - val_loss: 0.2015 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.1777 - val_loss: 0.0310 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.1231 - val_loss: 0.0070 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.6323 - val_loss: 4.9485 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 1.4239 - val_loss: 0.0064 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.0797 - val_loss: 0.0094 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.0627 - val_loss: 0.0055 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.0611 - val_loss: 0.0070 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.0467 - val_loss: 0.0054 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.0686 - val_loss: 0.0082 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.0390 - val_loss: 0.0052 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.0470 - val_loss: 0.0169 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.3901 - val_loss: 0.0055 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.0224 - val_loss: 0.0128 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.0367 - val_loss: 0.0865 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.0505 - val_loss: 0.0326 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.0677 - val_loss: 0.0593 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.0616 - val_loss: 0.0133 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2500 - val_loss: 0.0052 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.1632 - val_loss: 0.0135 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.0589 - val_loss: 0.0121 51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 34ms/step Sample raw predictions (after inverse transform and clipping): [1.78407 1.6539694 1.648487 1.751756 1.7345482] RMSE = 1.8927971 Validation R-squared for item 1445: -0.057062625885009766 53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 31ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=2.931734561920166 True y_val range (after inverse transform): min=0.0, max=10.0
-----------------------------------
Current item is 2012
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=9
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.6666666666666666
x_eval_time shape before reshape: (1627, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1907, 14)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (209, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (175, 20)
store_x_eval.shape: (203, 20)
Model: "sequential_134"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_269 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_174 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_270 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_175 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_133 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 116ms/step - loss: 15997.5273 - val_loss: 2057.2039 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 2488.5540 - val_loss: 315.2719 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 788.5375 - val_loss: 24.2098 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 47.0511 - val_loss: 14.1125 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 26.5127 - val_loss: 10.4657 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 17.3912 - val_loss: 2.6465 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 10.8316 - val_loss: 1.3057 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 7.3124 - val_loss: 2.7242 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 5.7847 - val_loss: 0.1239 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 4.8142 - val_loss: 0.1816 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 4.3286 - val_loss: 0.3363 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 3.5529 - val_loss: 0.1333 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 2.7549 - val_loss: 0.1304 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 2.2311 - val_loss: 0.7254 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 1.8125 - val_loss: 0.2678 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 1.4099 - val_loss: 0.1958 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 1.4868 - val_loss: 0.3266 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 1.4142 - val_loss: 0.8220 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 1.1983 - val_loss: 0.0364 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 1.1097 - val_loss: 0.6364 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.8681 - val_loss: 0.2359 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 0.8915 - val_loss: 0.1001 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 0.8201 - val_loss: 0.4480 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 0.7024 - val_loss: 0.0295 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 0.6076 - val_loss: 0.0335 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.6390 - val_loss: 0.2120 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.4247 - val_loss: 0.0486 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.3498 - val_loss: 0.5048 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 0.4375 - val_loss: 0.0752 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.5466 - val_loss: 0.0742 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.4307 - val_loss: 1.0079 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.5522 - val_loss: 0.0088 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 0.2648 - val_loss: 0.0374 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 0.2907 - val_loss: 0.0071 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.7496 - val_loss: 0.0560 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.3639 - val_loss: 1.6129 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.4689 - val_loss: 0.0187 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2262 - val_loss: 0.0095 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 0.4785 - val_loss: 0.2133 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.4165 - val_loss: 0.0789 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2578 - val_loss: 0.2049 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2013 - val_loss: 0.0300 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.1782 - val_loss: 0.2844 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2657 - val_loss: 0.0575 51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 34ms/step Sample raw predictions (after inverse transform and clipping): [0.4251839 0.49944162 1.1400505 0.93561727 0. ] RMSE = 0.81538653 Validation R-squared for item 2012: -0.2776836156845093 51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 32ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=1.5421051979064941 True y_val range (after inverse transform): min=0.0, max=6.0
-----------------------------------
Current item is 2168
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=10
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.5
x_eval_time shape before reshape: (1632, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1912, 14)
store_x_eval.shape: (206, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (213, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (185, 20)
Model: "sequential_135"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_271 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_176 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_272 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_177 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_134 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 118ms/step - loss: 34375.7148 - val_loss: 57160.7188 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 41967.8672 - val_loss: 438.1059 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 3281.5559 - val_loss: 310.9775 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 867.9604 - val_loss: 36.3859 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 343.6973 - val_loss: 40.3279 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 178.7780 - val_loss: 34.3111 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 293.1850 - val_loss: 35.2279 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 296.8963 - val_loss: 18.2245 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 167.3350 - val_loss: 10.5285 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 125.2502 - val_loss: 7.7910 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 80.4312 - val_loss: 2.7357 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 41.4211 - val_loss: 0.4691 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 25.4672 - val_loss: 1.0255 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 23.7747 - val_loss: 1.4716 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 15.4867 - val_loss: 1.5887 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 16.2243 - val_loss: 0.7170 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 9.0034 - val_loss: 3.1795 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 7.2614 - val_loss: 1.8916 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 5.9429 - val_loss: 0.1366 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 6.6018 - val_loss: 4.4103 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 5.8435 - val_loss: 20.4598 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 18.1962 - val_loss: 0.6939 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 2.2026 - val_loss: 0.0686 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 2.6305 - val_loss: 0.2914 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 2.0530 - val_loss: 0.0637 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 1.6149 - val_loss: 0.0192 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 1.6225 - val_loss: 1.9252 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 1.6975 - val_loss: 0.6123 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 1.6671 - val_loss: 0.5671 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 4.4208 - val_loss: 1.1488 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 2.6565 - val_loss: 0.6577 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 1.2814 - val_loss: 0.2639 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 1.6668 - val_loss: 0.0595 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 2.3514 - val_loss: 0.0309 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 1.8982 - val_loss: 0.2243 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 2.0716 - val_loss: 0.2693 52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 33ms/step Sample raw predictions (after inverse transform and clipping): [0. 0. 0. 0.49052805 0.78078806] RMSE = 1.1071262 Validation R-squared for item 2168: -6.757527828216553 51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 31ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=11.21563720703125 True y_val range (after inverse transform): min=0.0, max=5.0
-----------------------------------
Current item is 1050
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=23
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.6956521739130435
x_eval_time shape before reshape: (1643, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1923, 14)
store_x_eval.shape: (168, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (212, 20)
Model: "sequential_136"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_273 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_178 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_274 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_179 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_135 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 117ms/step - loss: 25401.0293 - val_loss: 5332.6982 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 7934.6445 - val_loss: 215.2274 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 841.3720 - val_loss: 54.8551 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 351.6442 - val_loss: 27.3799 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 199.8824 - val_loss: 14.0569 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 126.3875 - val_loss: 10.4290 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 87.8577 - val_loss: 7.3625 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 72.2663 - val_loss: 8.3979 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 53.3199 - val_loss: 5.0769 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 45.1260 - val_loss: 4.8873 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 33.2359 - val_loss: 3.4527 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 27.6061 - val_loss: 1.3221 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 23.9589 - val_loss: 1.7102 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 19.6147 - val_loss: 1.7267 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 19.1600 - val_loss: 1.2711 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 14.2480 - val_loss: 0.6413 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 12.4750 - val_loss: 0.8892 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 14.3111 - val_loss: 0.4509 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 8.3742 - val_loss: 0.5119 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 11.2719 - val_loss: 0.6389 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 7.2885 - val_loss: 0.5851 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 5.8204 - val_loss: 1.6331 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 4.8384 - val_loss: 0.4829 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 5.8830 - val_loss: 1.3201 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 4.2526 - val_loss: 0.0726 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 4.8251 - val_loss: 1.1619 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 4.4220 - val_loss: 0.2517 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 3.8236 - val_loss: 0.2093 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 2.9882 - val_loss: 0.4681 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 2.6417 - val_loss: 0.2830 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 2.3610 - val_loss: 0.1956 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 3.2775 - val_loss: 0.9988 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 1.7297 - val_loss: 0.5010 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 2.3525 - val_loss: 0.4577 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 1.7956 - val_loss: 4.2418 52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 35ms/step Sample raw predictions (after inverse transform and clipping): [ 6.332184 0.16677544 13.066532 9.544417 0. ] RMSE = 4.7892513 Validation R-squared for item 1050: -3.829493999481201 52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 32ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=21.054485321044922 True y_val range (after inverse transform): min=0.0, max=16.0
-----------------------------------
Current item is 1177
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=30
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.6
x_eval_time shape before reshape: (1623, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1903, 14)
store_x_eval.shape: (205, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (170, 20)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (207, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (166, 20)
Model: "sequential_137"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_275 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_180 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_276 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_181 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_136 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 117ms/step - loss: 44624.7773 - val_loss: 2902.3999 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 10833.3096 - val_loss: 703.4358 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 2914.1763 - val_loss: 324.6014 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 1799.7914 - val_loss: 170.4707 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 1145.2549 - val_loss: 154.1617 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 747.6458 - val_loss: 162.2749 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 553.6264 - val_loss: 120.6299 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 408.1453 - val_loss: 91.0298 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 318.3636 - val_loss: 48.3396 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 195.3276 - val_loss: 25.2634 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 111.6279 - val_loss: 11.2847 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 60.7731 - val_loss: 4.2480 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 28.6335 - val_loss: 1.2984 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 27.4946 - val_loss: 2.2459 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 19.7037 - val_loss: 1.3870 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 13.1685 - val_loss: 1.3600 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 9.4184 - val_loss: 0.7113 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 6.7274 - val_loss: 0.5823 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 5.6803 - val_loss: 0.6020 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 3.6509 - val_loss: 0.4290 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 3.2265 - val_loss: 0.2049 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 2.1720 - val_loss: 0.2734 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 2.0048 - val_loss: 0.1276 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 1.5857 - val_loss: 0.0609 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 1.3097 - val_loss: 0.0525 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 1.0153 - val_loss: 0.0848 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.9369 - val_loss: 0.0807 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.9297 - val_loss: 0.0257 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.7633 - val_loss: 0.0441 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.8792 - val_loss: 0.0930 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.6076 - val_loss: 0.0291 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.5268 - val_loss: 0.0247 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.4986 - val_loss: 0.0119 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.4578 - val_loss: 0.0114 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.4029 - val_loss: 0.1864 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.5384 - val_loss: 0.0157 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.3586 - val_loss: 0.0266 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.3895 - val_loss: 0.0467 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.2870 - val_loss: 0.0126 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2704 - val_loss: 0.1298 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.2669 - val_loss: 0.0110 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.2576 - val_loss: 0.0099 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2595 - val_loss: 0.0587 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.2122 - val_loss: 0.0054 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2208 - val_loss: 0.0052 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2342 - val_loss: 0.0228 Epoch 47/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2380 - val_loss: 0.0055 Epoch 48/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2208 - val_loss: 0.0084 Epoch 49/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2027 - val_loss: 0.0057 Epoch 50/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.1823 - val_loss: 0.0115 Epoch 51/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.3050 - val_loss: 0.0474 Epoch 52/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.1982 - val_loss: 0.0707 Epoch 53/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.2141 - val_loss: 0.0204 Epoch 54/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.1474 - val_loss: 0.0135 Epoch 55/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.1662 - val_loss: 0.0607 51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 33ms/step Sample raw predictions (after inverse transform and clipping): [1.0407993 1.7573586 1.4073275 1.1716727 1.2933131] RMSE = 2.8082244 Validation R-squared for item 1177: -0.34910428524017334 51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 31ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=11.239595413208008 True y_val range (after inverse transform): min=0.0, max=18.0
-----------------------------------
Current item is 2156
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=6
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.6666666666666666
x_eval_time shape before reshape: (1625, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1905, 14)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (172, 20)
store_x_eval.shape: (206, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (177, 20)
Model: "sequential_138"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_277 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_182 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_278 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_183 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_137 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 117ms/step - loss: 8290.8770 - val_loss: 111.9026 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 338.8893 - val_loss: 5.1481 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 61.8050 - val_loss: 2.0055 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 10.5168 - val_loss: 0.1029 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 1.3678 - val_loss: 0.1296 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.6785 - val_loss: 0.0467 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.3826 - val_loss: 0.3447 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2585 - val_loss: 0.0316 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.2597 - val_loss: 0.8128 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.3207 - val_loss: 0.0954 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.1307 - val_loss: 0.0302 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.1812 - val_loss: 0.7124 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2483 - val_loss: 0.0239 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.0689 - val_loss: 0.0073 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.1533 - val_loss: 0.0497 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2488 - val_loss: 0.0305 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.1323 - val_loss: 0.1712 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.0943 - val_loss: 0.0080 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2884 - val_loss: 0.0375 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.0428 - val_loss: 0.0261 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.0413 - val_loss: 0.0146 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.0307 - val_loss: 0.0494 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.5782 - val_loss: 0.0043 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.0226 - val_loss: 0.0089 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.0738 - val_loss: 0.0483 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.0444 - val_loss: 0.0798 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.0571 - val_loss: 0.0052 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.0633 - val_loss: 0.0446 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.0980 - val_loss: 0.0101 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.0292 - val_loss: 0.0216 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.0782 - val_loss: 0.0110 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.0816 - val_loss: 0.0627 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.0462 - val_loss: 1.2285 54/54 ━━━━━━━━━━━━━━━━━━━━ 2s 36ms/step Sample raw predictions (after inverse transform and clipping): [0.02438923 0.01139406 0.04834878 0.06852264 0.01114932] RMSE = 0.49918652 Validation R-squared for item 2156: -0.08563041687011719 51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 32ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=0.5729254484176636 True y_val range (after inverse transform): min=0.0, max=4.0
-----------------------------------
Current item is 1361
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=24
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.6666666666666666
x_eval_time shape before reshape: (1601, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1881, 14)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (174, 20)
Model: "sequential_139"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_279 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_184 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_280 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_185 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_138 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 13s 112ms/step - loss: 29882.0352 - val_loss: 119667.7656 Epoch 2/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 97016.4297 - val_loss: 119.2520 Epoch 3/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 1940.2581 - val_loss: 52.8342 Epoch 4/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 723.1902 - val_loss: 36.5917 Epoch 5/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 402.7668 - val_loss: 25.5489 Epoch 6/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 290.3178 - val_loss: 19.2731 Epoch 7/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 213.9572 - val_loss: 20.1421 Epoch 8/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 185.3600 - val_loss: 12.2804 Epoch 9/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 144.3360 - val_loss: 15.3426 Epoch 10/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 118.7076 - val_loss: 41.7046 Epoch 11/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 105.7787 - val_loss: 8.8359 Epoch 12/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 91.2208 - val_loss: 8.3054 Epoch 13/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 80.5774 - val_loss: 10.6796 Epoch 14/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 67.5216 - val_loss: 6.6743 Epoch 15/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 58.4710 - val_loss: 13.1262 Epoch 16/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 52.7549 - val_loss: 6.0644 Epoch 17/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 46.0605 - val_loss: 7.7990 Epoch 18/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 43.0764 - val_loss: 13.4443 Epoch 19/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 38.4902 - val_loss: 4.1586 Epoch 20/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 32.7660 - val_loss: 31.9534 Epoch 21/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 34.0813 - val_loss: 13.5719 Epoch 22/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 29.7181 - val_loss: 6.1791 Epoch 23/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 27.2596 - val_loss: 3.5494 Epoch 24/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 112ms/step - loss: 22.1809 - val_loss: 2.8846 Epoch 25/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 20.1342 - val_loss: 10.6376 Epoch 26/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 21.1633 - val_loss: 4.1040 Epoch 27/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 14.9510 - val_loss: 2.6717 Epoch 28/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 15.0132 - val_loss: 1.6112 Epoch 29/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 14.1537 - val_loss: 1.5618 Epoch 30/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 11.2768 - val_loss: 5.3655 Epoch 31/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 10.8906 - val_loss: 3.1102 Epoch 32/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 10.0422 - val_loss: 1.8317 Epoch 33/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 9.6504 - val_loss: 2.2949 Epoch 34/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 8.8335 - val_loss: 0.7166 Epoch 35/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 7.6829 - val_loss: 1.1615 Epoch 36/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 7.4833 - val_loss: 3.4228 Epoch 37/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 6.7174 - val_loss: 7.7666 Epoch 38/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 6.6711 - val_loss: 0.8106 Epoch 39/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 5.9581 - val_loss: 28.3848 Epoch 40/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 10.6577 - val_loss: 1.1211 Epoch 41/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 4.6302 - val_loss: 12.6691 Epoch 42/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 8.2593 - val_loss: 1.5008 Epoch 43/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 110ms/step - loss: 4.6629 - val_loss: 4.0521 Epoch 44/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 12s 111ms/step - loss: 4.3626 - val_loss: 4.1796 51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 33ms/step Sample raw predictions (after inverse transform and clipping): [0. 0. 0. 0. 9.498136] RMSE = 12.133267 Validation R-squared for item 1361: -26.97688102722168 51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 30ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=53.450992584228516 True y_val range (after inverse transform): min=0.0, max=16.0
-----------------------------------
Current item is 1074
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=18
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.4444444444444444
x_eval_time shape before reshape: (1618, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1898, 14)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (178, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (204, 20)
Model: "sequential_140"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_281 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_186 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_282 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_187 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_139 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 118ms/step - loss: 20917.5977 - val_loss: 523.7988 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 1820.9797 - val_loss: 77.7954 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 469.9118 - val_loss: 52.5772 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 193.7009 - val_loss: 10.2752 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 81.8956 - val_loss: 1.9827 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 39.7989 - val_loss: 2.4639 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 26.8196 - val_loss: 1.3595 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 15.0658 - val_loss: 0.3992 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 13.4954 - val_loss: 0.8631 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 10.6634 - val_loss: 0.6887 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 8.6248 - val_loss: 1.0273 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 7.2048 - val_loss: 0.1866 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 6.0716 - val_loss: 0.1143 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 4.3828 - val_loss: 0.0894 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 2.6461 - val_loss: 0.5092 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 2.9192 - val_loss: 0.5139 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 2.6219 - val_loss: 0.1771 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 1.5052 - val_loss: 0.1125 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 1.3561 - val_loss: 0.2117 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 1.1969 - val_loss: 0.0669 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.8832 - val_loss: 0.3111 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 0.8437 - val_loss: 0.0501 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.6223 - val_loss: 0.1746 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.5145 - val_loss: 0.0632 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.6009 - val_loss: 0.0918 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 0.5355 - val_loss: 0.0360 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.4732 - val_loss: 0.1401 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.4697 - val_loss: 0.0497 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.4169 - val_loss: 0.0670 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 0.3780 - val_loss: 0.0359 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 0.3514 - val_loss: 0.0534 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 0.3660 - val_loss: 0.0365 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 0.3136 - val_loss: 0.0169 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 0.2680 - val_loss: 0.0544 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 0.2176 - val_loss: 0.0372 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 0.2073 - val_loss: 0.7319 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 0.4145 - val_loss: 0.2465 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 0.2529 - val_loss: 0.0893 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 0.1809 - val_loss: 0.0397 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.1757 - val_loss: 0.0340 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 0.3816 - val_loss: 0.0143 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.1947 - val_loss: 0.0664 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.4427 - val_loss: 0.0350 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.1479 - val_loss: 0.1033 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.2252 - val_loss: 0.0262 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.1352 - val_loss: 0.2111 Epoch 47/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.1811 - val_loss: 0.0065 Epoch 48/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.1827 - val_loss: 0.0470 Epoch 49/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.1285 - val_loss: 0.0238 Epoch 50/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.1484 - val_loss: 0.0202 Epoch 51/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.1716 - val_loss: 0.0255 Epoch 52/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.2319 - val_loss: 0.0648 Epoch 53/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.2002 - val_loss: 0.0153 Epoch 54/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.4320 - val_loss: 0.1047 Epoch 55/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.4890 - val_loss: 0.1293 Epoch 56/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.5835 - val_loss: 0.1843 Epoch 57/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.1540 - val_loss: 0.0073 54/54 ━━━━━━━━━━━━━━━━━━━━ 2s 36ms/step Sample raw predictions (after inverse transform and clipping): [1.187185 1.1976706 1.1143035 0.7609577 0. ] RMSE = 1.288355 Validation R-squared for item 1074: -0.9366446733474731 51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 33ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=7.25835657119751 True y_val range (after inverse transform): min=0.0, max=8.0
-----------------------------------
Current item is 1265
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=12
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.6666666666666666
x_eval_time shape before reshape: (1656, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1936, 14)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (177, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (207, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (206, 20)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (206, 20)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (167, 20)
Model: "sequential_141"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_283 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_188 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_284 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_189 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_140 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 120ms/step - loss: 8322.6650 - val_loss: 116.4030 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 316.2222 - val_loss: 12.1824 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 83.1694 - val_loss: 2.9947 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 34.6315 - val_loss: 2.9784 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 17.8371 - val_loss: 0.6925 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 6.7460 - val_loss: 0.6268 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 6.0347 - val_loss: 2.5081 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 7.0590 - val_loss: 0.2629 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 3.4114 - val_loss: 0.1374 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 1.7789 - val_loss: 0.6894 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 1.3210 - val_loss: 0.1150 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 1.2440 - val_loss: 0.4577 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.8917 - val_loss: 0.0819 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.6527 - val_loss: 0.0689 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.5739 - val_loss: 0.0395 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.4467 - val_loss: 0.0326 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.4690 - val_loss: 0.0504 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.3381 - val_loss: 2.1390 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.6816 - val_loss: 0.8637 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.6758 - val_loss: 0.4268 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.4208 - val_loss: 0.6666 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.4116 - val_loss: 0.1016 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.3209 - val_loss: 0.0293 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.2099 - val_loss: 0.0552 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.4587 - val_loss: 0.5189 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.2738 - val_loss: 0.0280 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.2168 - val_loss: 0.0343 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.2138 - val_loss: 0.1443 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.4095 - val_loss: 0.0119 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.1434 - val_loss: 0.0508 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.2106 - val_loss: 0.1274 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.3286 - val_loss: 0.0400 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.2626 - val_loss: 0.0208 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.1257 - val_loss: 0.0119 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.2948 - val_loss: 0.0117 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 0.1368 - val_loss: 0.0915 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.3200 - val_loss: 0.0287 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.1398 - val_loss: 0.0318 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 0.1152 - val_loss: 0.0227 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.3421 - val_loss: 0.0348 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.2457 - val_loss: 0.0242 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.1664 - val_loss: 0.0225 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.3754 - val_loss: 0.0135 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.2271 - val_loss: 0.0262 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.1194 - val_loss: 0.0921 52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 39ms/step Sample raw predictions (after inverse transform and clipping): [0.7510407 0.7246634 0.77833104 0.8691641 1.2433431 ] RMSE = 1.1674118 Validation R-squared for item 1265: -0.31642043590545654 52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 33ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=7.536468029022217 True y_val range (after inverse transform): min=0.0, max=8.0
-----------------------------------
Current item is 2221
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=14
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.8571428571428571
x_eval_time shape before reshape: (1665, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1945, 14)
store_x_eval.shape: (171, 20)
store_x_eval.shape: (219, 20)
store_x_eval.shape: (175, 20)
store_x_eval.shape: (205, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (199, 20)
Model: "sequential_142"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_285 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_190 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_286 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_191 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_141 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 119ms/step - loss: 35925.5117 - val_loss: 1566.6349 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 2116.3655 - val_loss: 81.7741 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 430.7963 - val_loss: 136.3681 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 211.5713 - val_loss: 35.1650 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 118.7193 - val_loss: 23.1144 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 81.8371 - val_loss: 7.9310 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 57.1146 - val_loss: 7.6643 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 41.7110 - val_loss: 3.7283 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 32.5754 - val_loss: 3.0487 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 27.1959 - val_loss: 8.0489 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 23.3065 - val_loss: 2.1000 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 18.7051 - val_loss: 3.0002 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 15.3172 - val_loss: 7.1859 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 14.5911 - val_loss: 30.0302 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 16.8607 - val_loss: 2.2638 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 9.5578 - val_loss: 1.1437 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 7.9501 - val_loss: 1.1942 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 7.5098 - val_loss: 6.8789 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 8.8689 - val_loss: 2.8745 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 6.8338 - val_loss: 1.7127 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 5.2079 - val_loss: 0.3755 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 4.0529 - val_loss: 0.6279 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 3.6876 - val_loss: 0.8808 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 3.3246 - val_loss: 8.1875 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 4.5008 - val_loss: 5.7519 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 3.9211 - val_loss: 1.1800 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 2.2923 - val_loss: 1.8609 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 2.2766 - val_loss: 2.6087 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 2.1976 - val_loss: 1.4468 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 1.8459 - val_loss: 1.3376 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 1.4632 - val_loss: 1.9081 53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 35ms/step Sample raw predictions (after inverse transform and clipping): [1.9744182 0. 0. 0. 0. ] RMSE = 6.187594 Validation R-squared for item 2221: -96.90941619873047 53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 32ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=27.673616409301758 True y_val range (after inverse transform): min=0.0, max=12.0
-----------------------------------
Current item is 1164
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=14
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.6428571428571428
x_eval_time shape before reshape: (1639, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1919, 14)
store_x_eval.shape: (216, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (178, 20)
store_x_eval.shape: (183, 20)
Model: "sequential_143"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_287 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_192 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_288 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_193 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_142 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 115ms/step - loss: 11416.3867 - val_loss: 231.5361 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 1104.9559 - val_loss: 41.3923 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 177.8463 - val_loss: 17.2359 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 76.4523 - val_loss: 5.7385 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 38.7141 - val_loss: 3.5160 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 25.2371 - val_loss: 3.1355 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 16.6843 - val_loss: 6.0632 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 11.6400 - val_loss: 2.5194 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 6.7764 - val_loss: 1.4476 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 5.9609 - val_loss: 0.5749 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 3.3708 - val_loss: 2.2133 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 2.0049 - val_loss: 0.9450 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 1.2472 - val_loss: 0.3053 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 1.0738 - val_loss: 0.2450 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.9013 - val_loss: 2.0997 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.8767 - val_loss: 3.9912 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 1.1002 - val_loss: 0.0164 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.5083 - val_loss: 0.0855 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.3688 - val_loss: 0.0321 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.4162 - val_loss: 0.0231 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.4138 - val_loss: 0.3173 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.8374 - val_loss: 0.0505 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.5147 - val_loss: 0.0074 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.4933 - val_loss: 1.3405 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 1.1239 - val_loss: 0.0631 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.2173 - val_loss: 0.3794 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 114ms/step - loss: 0.3338 - val_loss: 0.0202 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.1948 - val_loss: 1.0111 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 1.0570 - val_loss: 1.2107 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.7285 - val_loss: 0.3282 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 0.6428 - val_loss: 0.0262 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 0.1974 - val_loss: 0.0265 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 113ms/step - loss: 5.8214 - val_loss: 0.1156 52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 35ms/step Sample raw predictions (after inverse transform and clipping): [0.08827338 0. 0.5581268 0. 0.04601315] RMSE = 1.1990379 Validation R-squared for item 1164: -0.08762538433074951 52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 31ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=1.6346347332000732 True y_val range (after inverse transform): min=0.0, max=9.0
-----------------------------------
Current item is 2106
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=6
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.6666666666666666
x_eval_time shape before reshape: (1644, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1924, 14)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (207, 20)
store_x_eval.shape: (174, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (210, 20)
store_x_eval.shape: (176, 20)
Model: "sequential_144"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_289 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_194 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_290 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_195 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_143 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 120ms/step - loss: 13085.9102 - val_loss: 315.2415 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 754.3457 - val_loss: 37.7731 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 170.2674 - val_loss: 30.6020 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 69.8272 - val_loss: 3.0887 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 30.7674 - val_loss: 1.3729 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 15.6875 - val_loss: 1.6926 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 9.5343 - val_loss: 2.3414 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 7.0649 - val_loss: 0.8414 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 4.9869 - val_loss: 0.3043 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 3.7756 - val_loss: 0.2602 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 2.4585 - val_loss: 0.1127 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 2.0239 - val_loss: 0.8605 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 1.5819 - val_loss: 0.0411 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 1.4185 - val_loss: 0.0205 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 1.1651 - val_loss: 0.9578 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 1.0397 - val_loss: 1.2689 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 1.1777 - val_loss: 0.0219 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 1.2012 - val_loss: 0.0575 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.6238 - val_loss: 0.0101 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 0.7637 - val_loss: 0.1181 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.6825 - val_loss: 0.5891 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.6575 - val_loss: 0.0536 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 0.5505 - val_loss: 0.4619 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 0.6440 - val_loss: 0.3514 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 0.4850 - val_loss: 1.8509 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 0.8169 - val_loss: 0.0098 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.3760 - val_loss: 0.0681 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 0.3862 - val_loss: 0.0446 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.4202 - val_loss: 0.1380 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 0.3742 - val_loss: 0.0890 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 0.2327 - val_loss: 0.0044 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.2420 - val_loss: 0.0302 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 0.3971 - val_loss: 0.0085 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.2045 - val_loss: 0.0392 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.4587 - val_loss: 0.0046 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.3240 - val_loss: 0.0864 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 0.2062 - val_loss: 0.0659 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 0.2345 - val_loss: 0.0192 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 0.1717 - val_loss: 0.3709 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 118ms/step - loss: 0.2253 - val_loss: 0.0057 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.2487 - val_loss: 0.0861 53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 35ms/step Sample raw predictions (after inverse transform and clipping): [0. 0.13146748 0.08906481 0.16355906 0.26161784] RMSE = 0.40588656 Validation R-squared for item 2106: -0.3254774808883667 52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 32ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=0.9727151393890381 True y_val range (after inverse transform): min=0.0, max=4.0
-----------------------------------
Current item is 2199
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=16
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.75
x_eval_time shape before reshape: (1687, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1967, 14)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (209, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (206, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (215, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (179, 20)
Model: "sequential_145"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_291 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_196 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_292 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_197 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_144 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 123ms/step - loss: 8127.8408 - val_loss: 680.4302 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 592.5879 - val_loss: 26.4985 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 78.3791 - val_loss: 4.8960 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 22.6474 - val_loss: 0.5114 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 3.1195 - val_loss: 0.2576 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 1.3928 - val_loss: 0.0700 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.8203 - val_loss: 0.0289 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.4228 - val_loss: 0.0128 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.2294 - val_loss: 0.0178 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.1942 - val_loss: 0.0139 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.1572 - val_loss: 0.0056 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.1184 - val_loss: 0.0124 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.0911 - val_loss: 0.0071 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.1161 - val_loss: 0.0091 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.0981 - val_loss: 0.0092 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.0531 - val_loss: 0.0119 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.0599 - val_loss: 0.0043 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.0555 - val_loss: 0.0067 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.0608 - val_loss: 0.0099 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.0232 - val_loss: 0.0049 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.0243 - val_loss: 0.0040 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.0153 - val_loss: 0.0045 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.0195 - val_loss: 0.0033 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.0194 - val_loss: 0.0032 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.0147 - val_loss: 0.0124 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.0257 - val_loss: 0.0039 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.0134 - val_loss: 0.0036 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.0271 - val_loss: 0.0033 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.0139 - val_loss: 0.0046 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.0148 - val_loss: 0.0037 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.0129 - val_loss: 0.0098 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.0175 - val_loss: 0.0321 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.0234 - val_loss: 0.0142 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.0226 - val_loss: 0.0052 51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 37ms/step Sample raw predictions (after inverse transform and clipping): [0.65381753 0.6351657 0.5634749 0.59005505 0.6541003 ] RMSE = 1.1718154 Validation R-squared for item 2199: -0.02255237102508545 53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 33ms/step Predicted y_val range (after inverse transform and clipping): min=0.3950556814670563, max=1.8669464588165283 True y_val range (after inverse transform): min=0.0, max=12.0
-----------------------------------
Current item is 1504
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=12
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.5833333333333333
x_eval_time shape before reshape: (1636, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1916, 14)
store_x_eval.shape: (178, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (219, 20)
store_x_eval.shape: (175, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (193, 20)
Model: "sequential_146"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_293 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_198 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_294 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_199 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_145 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 123ms/step - loss: 27361.2969 - val_loss: 856.7946 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 2915.4482 - val_loss: 258.5603 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 872.1925 - val_loss: 80.1317 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 383.4228 - val_loss: 68.7992 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 291.5343 - val_loss: 23.5859 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 163.3622 - val_loss: 16.9654 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 105.6384 - val_loss: 10.8264 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 67.3603 - val_loss: 5.3923 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 49.4094 - val_loss: 4.2467 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 32.3661 - val_loss: 4.8813 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 28.7909 - val_loss: 1.6952 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 20.0658 - val_loss: 2.3409 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 14.8445 - val_loss: 0.9293 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 10.2542 - val_loss: 0.5404 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 8.8571 - val_loss: 0.3689 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 6.2965 - val_loss: 0.3785 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 5.3717 - val_loss: 0.3697 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 3.6498 - val_loss: 0.5677 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 4.1552 - val_loss: 0.4239 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 3.1401 - val_loss: 0.2402 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 2.4635 - val_loss: 0.3073 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 1.7773 - val_loss: 0.0999 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 1.4679 - val_loss: 0.3469 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 1.6180 - val_loss: 0.1112 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 1.0323 - val_loss: 0.1014 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 1.0847 - val_loss: 0.1046 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.7567 - val_loss: 0.0410 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.7328 - val_loss: 0.3103 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.5946 - val_loss: 0.0905 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.4114 - val_loss: 0.1110 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.3836 - val_loss: 0.1558 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.4129 - val_loss: 0.0133 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.4139 - val_loss: 0.0355 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.2908 - val_loss: 0.2765 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.3250 - val_loss: 0.0153 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.2860 - val_loss: 0.0246 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.2625 - val_loss: 0.0889 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.2474 - val_loss: 0.0124 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.1995 - val_loss: 0.0312 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.2089 - val_loss: 0.1006 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.2553 - val_loss: 0.0941 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.2001 - val_loss: 0.2058 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.1543 - val_loss: 0.0185 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.0926 - val_loss: 0.0092 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.0923 - val_loss: 0.1669 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.2628 - val_loss: 0.0314 Epoch 47/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.1502 - val_loss: 0.1474 Epoch 48/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.2291 - val_loss: 0.0129 Epoch 49/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.1486 - val_loss: 0.0164 Epoch 50/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.0691 - val_loss: 0.0339 Epoch 51/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.0772 - val_loss: 0.0177 Epoch 52/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.1521 - val_loss: 0.0114 Epoch 53/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.0529 - val_loss: 0.0179 Epoch 54/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.1941 - val_loss: 0.7078 53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 37ms/step Sample raw predictions (after inverse transform and clipping): [0.7461023 0.48899382 0.37014005 0.8506138 0.88745767] RMSE = 0.9063988 Validation R-squared for item 1504: -0.39566922187805176 52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 34ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=7.653149604797363 True y_val range (after inverse transform): min=0.0, max=7.0
-----------------------------------
Current item is 1217
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=17
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.7058823529411764
x_eval_time shape before reshape: (1652, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1932, 14)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (168, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (207, 20)
Model: "sequential_147"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_295 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_200 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_296 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_201 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_146 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 117ms/step - loss: 26720.2051 - val_loss: 3780.3308 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 10097.9482 - val_loss: 90.9688 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 573.0079 - val_loss: 35.5644 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 184.0097 - val_loss: 15.6040 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 117ms/step - loss: 91.8896 - val_loss: 4.3280 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 23.9071 - val_loss: 1.6444 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 13.0078 - val_loss: 4.6116 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 4.8410 - val_loss: 0.3697 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 2.7283 - val_loss: 0.1159 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 1.7987 - val_loss: 0.1589 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 3.2258 - val_loss: 1.3163 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 4.1560 - val_loss: 0.1219 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 2.8311 - val_loss: 1.0277 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 2.3933 - val_loss: 0.4552 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 1.8272 - val_loss: 0.0424 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 1.4311 - val_loss: 0.0550 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 1.1812 - val_loss: 0.2172 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 1.1214 - val_loss: 0.2580 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 1.0720 - val_loss: 0.1364 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 2.2061 - val_loss: 0.0444 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 0.7031 - val_loss: 0.3191 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 0.7277 - val_loss: 0.2744 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 115ms/step - loss: 1.3403 - val_loss: 1.0376 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 1.3888 - val_loss: 0.3477 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 12s 116ms/step - loss: 0.8899 - val_loss: 0.2706 51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 36ms/step Sample raw predictions (after inverse transform and clipping): [0. 0. 0. 1.4293045 0. ] RMSE = 1.779017 Validation R-squared for item 1217: -0.494115948677063 52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 33ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=15.705046653747559 True y_val range (after inverse transform): min=0.0, max=12.0
-----------------------------------
Current item is 2416
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=16
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.625
x_eval_time shape before reshape: (1675, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1955, 14)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (230, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (173, 20)
store_x_eval.shape: (214, 20)
Model: "sequential_148"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_297 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_202 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_298 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_203 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_147 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 123ms/step - loss: 9094.2695 - val_loss: 415.5934 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 817.4766 - val_loss: 68.2044 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 223.5820 - val_loss: 26.5361 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 63.4532 - val_loss: 4.5100 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 11.2580 - val_loss: 0.4124 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 3.6658 - val_loss: 0.6942 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 1.6783 - val_loss: 0.1331 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.8753 - val_loss: 0.0372 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 0.6898 - val_loss: 0.0598 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.5096 - val_loss: 0.0233 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.5203 - val_loss: 0.0736 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.1902 - val_loss: 0.0234 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.2236 - val_loss: 0.3478 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.2463 - val_loss: 0.0554 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.1667 - val_loss: 0.0756 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 0.2987 - val_loss: 0.1157 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 0.1687 - val_loss: 0.0059 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 0.1190 - val_loss: 0.2646 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 0.2618 - val_loss: 0.0237 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 0.1134 - val_loss: 0.0334 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 0.1219 - val_loss: 0.0283 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 0.0577 - val_loss: 0.1656 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 0.1342 - val_loss: 0.1053 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 138ms/step - loss: 0.0950 - val_loss: 0.0247 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 0.1517 - val_loss: 0.0077 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 0.0615 - val_loss: 0.1031 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 0.1172 - val_loss: 0.0291 49/49 ━━━━━━━━━━━━━━━━━━━━ 2s 38ms/step Sample raw predictions (after inverse transform and clipping): [0.7828205 0.94615453 1.2767978 0.6047141 1.3725545 ] RMSE = 1.471043 Validation R-squared for item 2416: -1.8352313041687012 53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 35ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=40.2111701965332 True y_val range (after inverse transform): min=0.0, max=10.0
-----------------------------------
Current item is 1144
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=10
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.9
x_eval_time shape before reshape: (1633, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1913, 14)
store_x_eval.shape: (177, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (208, 20)
store_x_eval.shape: (177, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (192, 20)
Model: "sequential_149"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_299 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_204 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_300 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_205 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_148 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 15s 128ms/step - loss: 13991.3867 - val_loss: 1560.0195 Epoch 2/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 2702.5930 - val_loss: 203.7880 Epoch 3/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 673.2391 - val_loss: 93.6143 Epoch 4/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 262.7306 - val_loss: 37.9598 Epoch 5/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 121.4239 - val_loss: 18.7477 Epoch 6/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 61.3078 - val_loss: 9.5012 Epoch 7/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 30.0997 - val_loss: 4.7299 Epoch 8/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 17.9337 - val_loss: 1.7755 Epoch 9/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 8.0625 - val_loss: 0.6316 Epoch 10/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 3.9434 - val_loss: 0.3600 Epoch 11/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 2.0804 - val_loss: 0.3424 Epoch 12/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 1.4151 - val_loss: 0.2496 Epoch 13/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.9892 - val_loss: 0.1777 Epoch 14/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.8307 - val_loss: 0.1760 Epoch 15/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.5506 - val_loss: 0.1155 Epoch 16/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.4888 - val_loss: 0.1034 Epoch 17/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 5.2468 - val_loss: 0.2718 Epoch 18/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.8818 - val_loss: 0.0735 Epoch 19/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 0.5547 - val_loss: 0.0859 Epoch 20/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 0.5271 - val_loss: 0.0386 Epoch 21/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 0.4540 - val_loss: 0.0330 Epoch 22/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 128ms/step - loss: 0.2596 - val_loss: 0.0387 Epoch 23/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.2260 - val_loss: 0.0341 Epoch 24/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.2093 - val_loss: 0.0242 Epoch 25/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.1605 - val_loss: 0.0276 Epoch 26/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.1491 - val_loss: 0.0251 Epoch 27/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 128ms/step - loss: 0.1297 - val_loss: 0.0288 Epoch 28/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.1378 - val_loss: 0.1528 Epoch 29/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 120ms/step - loss: 0.1267 - val_loss: 0.0164 Epoch 30/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 12s 118ms/step - loss: 0.0927 - val_loss: 0.0140 Epoch 31/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 119ms/step - loss: 0.0844 - val_loss: 0.0131 Epoch 32/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 121ms/step - loss: 0.0760 - val_loss: 0.0150 Epoch 33/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.0777 - val_loss: 0.0288 Epoch 34/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 0.0765 - val_loss: 0.0817 Epoch 35/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.0841 - val_loss: 0.0966 Epoch 36/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.1430 - val_loss: 0.0739 Epoch 37/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.0872 - val_loss: 0.0452 Epoch 38/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.1513 - val_loss: 0.0465 Epoch 39/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 0.1126 - val_loss: 0.1210 Epoch 40/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.0793 - val_loss: 0.2476 Epoch 41/100 105/105 ━━━━━━━━━━━━━━━━━━━━ 13s 128ms/step - loss: 0.1122 - val_loss: 0.0142 54/54 ━━━━━━━━━━━━━━━━━━━━ 2s 39ms/step Sample raw predictions (after inverse transform and clipping): [1.1958846 1.1057892 0. 0.8252209 0. ] RMSE = 0.94303274 Validation R-squared for item 1144: -0.20381522178649902 52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 34ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=4.8194427490234375 True y_val range (after inverse transform): min=0.0, max=9.0
-----------------------------------
Current item is 2054
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=10
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.6000000000000001
x_eval_time shape before reshape: (1627, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1907, 14)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (169, 20)
store_x_eval.shape: (208, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (169, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (194, 20)
Model: "sequential_150"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_301 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_206 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_302 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_207 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_149 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 20s 128ms/step - loss: 27475.6855 - val_loss: 385.4841 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 1731.7477 - val_loss: 145.0212 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 376.2201 - val_loss: 36.6563 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 182.3835 - val_loss: 14.9190 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 79.0177 - val_loss: 5.6637 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 48.6424 - val_loss: 3.9952 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 23.4958 - val_loss: 0.7651 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 11.3417 - val_loss: 1.3950 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 8.4354 - val_loss: 2.6701 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 6.2758 - val_loss: 0.6920 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 4.9987 - val_loss: 0.0691 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 3.7068 - val_loss: 0.2098 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 3.4165 - val_loss: 0.0481 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 2.8183 - val_loss: 0.3777 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 2.7648 - val_loss: 0.1820 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 2.4915 - val_loss: 0.1731 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 595.6979 - val_loss: 74.5545 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 156.7945 - val_loss: 0.0931 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 5.0259 - val_loss: 0.0257 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 1.5664 - val_loss: 0.1607 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 2.7072 - val_loss: 0.0241 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.6687 - val_loss: 0.1009 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 0.5952 - val_loss: 0.0546 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 0.5457 - val_loss: 0.0326 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.3436 - val_loss: 0.0229 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.3451 - val_loss: 0.0460 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 0.3660 - val_loss: 0.0091 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 0.2294 - val_loss: 0.0457 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.3980 - val_loss: 0.0094 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 0.1786 - val_loss: 0.0465 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.4097 - val_loss: 0.0311 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.1797 - val_loss: 0.0082 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.2064 - val_loss: 0.0175 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 0.1093 - val_loss: 0.0116 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.1666 - val_loss: 0.0126 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 0.0948 - val_loss: 0.0307 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.1528 - val_loss: 0.0110 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.1298 - val_loss: 0.0131 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.0772 - val_loss: 0.0078 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.0870 - val_loss: 0.0115 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.1043 - val_loss: 0.0930 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 0.1805 - val_loss: 0.0221 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.0689 - val_loss: 0.0487 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.8723 - val_loss: 0.1354 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.1192 - val_loss: 0.0574 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.1960 - val_loss: 0.3166 Epoch 47/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 1.5531 - val_loss: 0.9376 Epoch 48/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.4355 - val_loss: 0.0117 Epoch 49/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.0319 - val_loss: 0.0086 50/50 ━━━━━━━━━━━━━━━━━━━━ 2s 39ms/step Sample raw predictions (after inverse transform and clipping): [0.22629541 0.39531204 0.40688184 0.5427511 0.15686205] RMSE = 0.78427696 Validation R-squared for item 2054: -0.034137725830078125 51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 35ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=0.8365244269371033 True y_val range (after inverse transform): min=0.0, max=5.0
-----------------------------------
Current item is 1331
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=15
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.7333333333333333
x_eval_time shape before reshape: (1646, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1926, 14)
store_x_eval.shape: (206, 20)
store_x_eval.shape: (163, 20)
store_x_eval.shape: (217, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (178, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (207, 20)
Model: "sequential_151"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_303 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_208 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_304 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_209 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_150 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 124ms/step - loss: 33926.3984 - val_loss: 914.1102 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 122ms/step - loss: 4459.9355 - val_loss: 256.1666 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 1583.3724 - val_loss: 170.3104 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 384.4293 - val_loss: 9.6343 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 58.0279 - val_loss: 3.2104 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 22.2286 - val_loss: 1.0264 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 11.5960 - val_loss: 2.0818 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 8.0785 - val_loss: 0.5921 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 6.4235 - val_loss: 0.6774 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 5.6328 - val_loss: 0.8505 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 4.5361 - val_loss: 0.5503 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 4.3147 - val_loss: 3.5263 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 4.4916 - val_loss: 0.4098 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 4.3460 - val_loss: 0.2842 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 3.0457 - val_loss: 3.4340 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 3.1861 - val_loss: 0.2413 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 2.4746 - val_loss: 0.6129 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 2.4666 - val_loss: 2.3787 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 2.6748 - val_loss: 0.3426 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 1.8830 - val_loss: 1.4843 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 2.2513 - val_loss: 0.1697 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 1.8892 - val_loss: 0.1997 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 1.7857 - val_loss: 0.4264 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 1.5469 - val_loss: 0.2181 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 1.6987 - val_loss: 0.2123 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 1.4944 - val_loss: 0.2413 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 1.4271 - val_loss: 0.1537 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 1.3516 - val_loss: 0.8287 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 1.3448 - val_loss: 0.2499 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 1.3195 - val_loss: 0.1503 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 1.1111 - val_loss: 1.8026 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 2.3117 - val_loss: 0.0976 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.9608 - val_loss: 1.1050 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 1.3426 - val_loss: 1.3850 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 1.7060 - val_loss: 0.0908 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.8956 - val_loss: 0.4863 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 1.3033 - val_loss: 0.0869 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.7902 - val_loss: 0.1251 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.7943 - val_loss: 0.1434 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 1.0370 - val_loss: 0.6027 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 1.0427 - val_loss: 0.2717 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.6978 - val_loss: 0.1270 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.6883 - val_loss: 0.8568 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.9052 - val_loss: 0.0723 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 0.6780 - val_loss: 1.1747 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.8080 - val_loss: 0.0827 Epoch 47/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.5627 - val_loss: 0.4564 Epoch 48/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.6854 - val_loss: 0.2399 Epoch 49/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.8789 - val_loss: 0.1600 Epoch 50/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.6014 - val_loss: 0.4738 Epoch 51/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.5916 - val_loss: 0.9112 Epoch 52/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.8351 - val_loss: 0.1387 Epoch 53/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 0.2173 - val_loss: 0.0188 Epoch 54/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.1577 - val_loss: 0.0449 Epoch 55/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.0473 - val_loss: 0.0815 Epoch 56/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.1048 - val_loss: 0.0296 Epoch 57/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.1859 - val_loss: 0.0340 Epoch 58/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 1.2796 - val_loss: 0.0149 Epoch 59/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 0.0680 - val_loss: 0.1390 Epoch 60/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.0594 - val_loss: 0.0846 Epoch 61/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 123ms/step - loss: 0.1815 - val_loss: 0.1873 Epoch 62/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 0.1672 - val_loss: 0.1747 Epoch 63/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.1157 - val_loss: 0.0232 Epoch 64/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.1144 - val_loss: 0.2917 Epoch 65/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.1919 - val_loss: 0.0157 Epoch 66/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.2706 - val_loss: 0.0184 Epoch 67/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 0.0770 - val_loss: 0.0240 Epoch 68/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 124ms/step - loss: 0.0683 - val_loss: 0.2970 53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 38ms/step Sample raw predictions (after inverse transform and clipping): [2.0578766 1.9396904 1.3884815 1.9469295 1.4092681] RMSE = 1.8556609 Validation R-squared for item 1331: -0.5182192325592041 52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 35ms/step Predicted y_val range (after inverse transform and clipping): min=0.24021461606025696, max=3.5960328578948975 True y_val range (after inverse transform): min=0.0, max=11.0
-----------------------------------
Current item is 2377
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=17
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.5882352941176471
x_eval_time shape before reshape: (1605, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1885, 14)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (209, 20)
store_x_eval.shape: (173, 20)
store_x_eval.shape: (168, 20)
store_x_eval.shape: (175, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (200, 20)
Model: "sequential_152"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_305 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_210 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_306 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_211 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_151 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 129ms/step - loss: 4183.4238 - val_loss: 61.1546 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 125.9009 - val_loss: 6.4626 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 24.7645 - val_loss: 1.6798 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 8.9117 - val_loss: 0.5688 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 4.4383 - val_loss: 0.3184 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 2.0638 - val_loss: 0.1638 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 1.2701 - val_loss: 0.1484 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 0.8496 - val_loss: 0.0511 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 0.4806 - val_loss: 0.0154 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.4489 - val_loss: 0.0086 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.2087 - val_loss: 0.0204 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.2262 - val_loss: 0.0363 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 0.2234 - val_loss: 0.0065 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.1551 - val_loss: 0.0049 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.1147 - val_loss: 0.0031 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.1036 - val_loss: 0.0042 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.1305 - val_loss: 0.0019 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.1214 - val_loss: 0.0251 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.0785 - val_loss: 0.0095 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.1116 - val_loss: 0.0457 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.0568 - val_loss: 0.0035 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.0443 - val_loss: 0.0346 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.1041 - val_loss: 5.7372e-04 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.1081 - val_loss: 0.0351 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.0428 - val_loss: 0.0235 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 1.6046 - val_loss: 0.0064 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 0.2737 - val_loss: 0.0024 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.1303 - val_loss: 0.0063 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 0.0509 - val_loss: 0.0014 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 0.0419 - val_loss: 5.9376e-04 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.0357 - val_loss: 9.2390e-04 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.0159 - val_loss: 0.0012 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 0.0273 - val_loss: 7.9883e-04 52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 38ms/step Sample raw predictions (after inverse transform and clipping): [0. 0.12574503 0. 0.08638018 0.12740503] RMSE = 0.67207265 Validation R-squared for item 2377: -0.11461794376373291 51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 35ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=1.837878942489624 True y_val range (after inverse transform): min=0.0, max=10.0
-----------------------------------
Current item is 2124
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=13
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.5384615384615385
x_eval_time shape before reshape: (1640, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1920, 14)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (181, 20)
Model: "sequential_153"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_307 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_212 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_308 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_213 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_152 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 131ms/step - loss: 77748.6953 - val_loss: 8623.4561 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 18687.0879 - val_loss: 220.3259 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 1667.3567 - val_loss: 58.0999 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 495.5660 - val_loss: 26.3111 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 276.6446 - val_loss: 27.3185 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 201.9171 - val_loss: 16.6310 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 186.8736 - val_loss: 17.3321 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 128.8895 - val_loss: 19.3532 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 56.3507 - val_loss: 1.9922 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 30.2316 - val_loss: 1.3769 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 27.8897 - val_loss: 1.8490 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 21.0099 - val_loss: 2.3165 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 20.1979 - val_loss: 0.8273 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 17.4124 - val_loss: 0.7523 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 14.0294 - val_loss: 1.8645 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 14.6008 - val_loss: 8.3957 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 125ms/step - loss: 12.8100 - val_loss: 6.7560 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 11.9072 - val_loss: 0.6424 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 8.0843 - val_loss: 0.8836 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 7.3480 - val_loss: 0.5911 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 7.3884 - val_loss: 1.5969 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 10.3756 - val_loss: 2.5911 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 8.1578 - val_loss: 0.3387 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 6.8560 - val_loss: 0.5487 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 9.0729 - val_loss: 0.6945 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 4.6878 - val_loss: 0.5543 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 4.5846 - val_loss: 0.5475 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 5.1290 - val_loss: 1.3337 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 3.8830 - val_loss: 2.7092 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 4.9415 - val_loss: 0.3074 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 4.9709 - val_loss: 0.2104 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 3.4889 - val_loss: 5.6649 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 4.2593 - val_loss: 4.3782 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 5.0624 - val_loss: 0.2555 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 5.4932 - val_loss: 0.4498 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 6.7210 - val_loss: 0.9077 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 2.6797 - val_loss: 0.6467 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 4.2507 - val_loss: 0.1367 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 2.0683 - val_loss: 1.6247 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 3.3197 - val_loss: 1.4868 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 2.7358 - val_loss: 0.2415 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 2.3842 - val_loss: 3.6343 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 2.7224 - val_loss: 0.3371 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 1.9495 - val_loss: 0.2440 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 1.8159 - val_loss: 0.4638 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 1.9081 - val_loss: 5.4902 Epoch 47/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 2.6322 - val_loss: 0.5447 Epoch 48/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 2.1412 - val_loss: 7.7495 52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 39ms/step Sample raw predictions (after inverse transform and clipping): [0. 3.7810516 2.1341872 0. 0. ] RMSE = 2.3113859 Validation R-squared for item 2124: -8.466473579406738 52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 36ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=22.486120223999023 True y_val range (after inverse transform): min=0.0, max=7.000000476837158
-----------------------------------
Current item is 2101
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=6
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.5
x_eval_time shape before reshape: (1624, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1904, 14)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (177, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (176, 20)
Model: "sequential_154"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_309 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_214 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_310 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_215 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_153 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 131ms/step - loss: 4596.3770 - val_loss: 95.3279 Epoch 2/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 201.1535 - val_loss: 8.1043 Epoch 3/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 48.1081 - val_loss: 2.2798 Epoch 4/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 21.9064 - val_loss: 2.8067 Epoch 5/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 12.0960 - val_loss: 0.7991 Epoch 6/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 6.6001 - val_loss: 0.4042 Epoch 7/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 4.4514 - val_loss: 0.3438 Epoch 8/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 2.7699 - val_loss: 1.0724 Epoch 9/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 2.0883 - val_loss: 0.4742 Epoch 10/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 1.5510 - val_loss: 0.5974 Epoch 11/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 1.0658 - val_loss: 0.0939 Epoch 12/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.8079 - val_loss: 0.0974 Epoch 13/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.5910 - val_loss: 0.0541 Epoch 14/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.5637 - val_loss: 0.2343 Epoch 15/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.4537 - val_loss: 0.0294 Epoch 16/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 0.4189 - val_loss: 1.2565 Epoch 17/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.5113 - val_loss: 0.0727 Epoch 18/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.3016 - val_loss: 0.5314 Epoch 19/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.3707 - val_loss: 0.0054 Epoch 20/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.2245 - val_loss: 0.0476 Epoch 21/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.2129 - val_loss: 0.2784 Epoch 22/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.2542 - val_loss: 0.0435 Epoch 23/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.1911 - val_loss: 0.0103 Epoch 24/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.1833 - val_loss: 0.0141 Epoch 25/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.2151 - val_loss: 0.1160 Epoch 26/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.1724 - val_loss: 0.2750 Epoch 27/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.2971 - val_loss: 0.0051 Epoch 28/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.1426 - val_loss: 0.0401 Epoch 29/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 0.1889 - val_loss: 0.0094 Epoch 30/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 0.1121 - val_loss: 0.0686 Epoch 31/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 0.3033 - val_loss: 0.0050 Epoch 32/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.0776 - val_loss: 0.0053 Epoch 33/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.0852 - val_loss: 0.0754 Epoch 34/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.1178 - val_loss: 0.0974 Epoch 35/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.1675 - val_loss: 0.0064 Epoch 36/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.1183 - val_loss: 0.0184 Epoch 37/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.1062 - val_loss: 0.0494 Epoch 38/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.4449 - val_loss: 0.4500 Epoch 39/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.1324 - val_loss: 0.0079 Epoch 40/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.1745 - val_loss: 0.1996 Epoch 41/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.1124 - val_loss: 0.0072 49/49 ━━━━━━━━━━━━━━━━━━━━ 2s 40ms/step Sample raw predictions (after inverse transform and clipping): [0. 0. 0. 0. 0.08559164] RMSE = 0.4084564 Validation R-squared for item 2101: -1.6216132640838623 51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 36ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=3.839362144470215 True y_val range (after inverse transform): min=0.0, max=3.0
-----------------------------------
Current item is 1343
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=53
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.33962264150943394
x_eval_time shape before reshape: (1581, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1861, 14)
store_x_eval.shape: (169, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (168, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (184, 20)
Model: "sequential_155"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_311 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_216 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_312 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_217 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_154 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 135ms/step - loss: 11506.0781 - val_loss: 34.2771 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 180.9903 - val_loss: 18.6587 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 61.4608 - val_loss: 10.0060 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 29.9688 - val_loss: 2.3365 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 7.8875 - val_loss: 0.4862 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 3.9073 - val_loss: 0.1897 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 1.6753 - val_loss: 0.0762 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 1.1489 - val_loss: 0.3866 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 1.1062 - val_loss: 0.1158 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.6748 - val_loss: 0.0187 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.4190 - val_loss: 0.0112 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.4907 - val_loss: 0.0942 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.2798 - val_loss: 0.0072 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.2238 - val_loss: 0.0058 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.1624 - val_loss: 0.0152 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 0.3529 - val_loss: 0.2461 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 0.1941 - val_loss: 0.0068 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.1258 - val_loss: 0.1571 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.1621 - val_loss: 0.1175 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.1986 - val_loss: 0.0719 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.1441 - val_loss: 0.0986 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.4754 - val_loss: 0.0247 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.3737 - val_loss: 0.0152 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.2390 - val_loss: 0.0378 54/54 ━━━━━━━━━━━━━━━━━━━━ 2s 39ms/step Sample raw predictions (after inverse transform and clipping): [0.35500795 2.3920536 2.8993986 3.1933334 2.555277 ] RMSE = 2.9711812 Validation R-squared for item 1343: -0.7652384042739868 50/50 ━━━━━━━━━━━━━━━━━━━━ 2s 36ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=19.155323028564453 True y_val range (after inverse transform): min=0.0, max=18.0
-----------------------------------
Current item is 1282
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=10
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.1
x_eval_time shape before reshape: (1656, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1936, 14)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (212, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (175, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (218, 20)
Model: "sequential_156"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_313 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_218 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_314 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_219 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_155 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 130ms/step - loss: 24126.5449 - val_loss: 37572.9062 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 27283.8320 - val_loss: 210.6883 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 1087.2947 - val_loss: 112.8808 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 318.7560 - val_loss: 79.4087 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 148.5492 - val_loss: 15.4328 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 86.4486 - val_loss: 11.7265 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 55.4236 - val_loss: 14.5651 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 46.3208 - val_loss: 9.2457 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 36.5434 - val_loss: 12.0495 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 22.5853 - val_loss: 27.1561 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 19.8319 - val_loss: 7.3594 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 15.0220 - val_loss: 3.6585 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 12.8204 - val_loss: 2.5105 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 11.2537 - val_loss: 2.6146 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 9.9513 - val_loss: 3.3424 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 11.0742 - val_loss: 19.7970 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 14.1610 - val_loss: 1.0671 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 6.2322 - val_loss: 1.1139 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 5.2772 - val_loss: 0.6742 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 4.7511 - val_loss: 0.5298 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 4.4277 - val_loss: 5.4667 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 4.3241 - val_loss: 1.6676 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 3.4990 - val_loss: 0.5113 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 3.1932 - val_loss: 0.4288 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 2.4783 - val_loss: 1.5986 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 2.7440 - val_loss: 2.5969 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 2.4784 - val_loss: 0.1581 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 1.9538 - val_loss: 0.1912 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 1.6920 - val_loss: 1.8613 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 4.8208 - val_loss: 0.2505 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 127ms/step - loss: 1.4823 - val_loss: 0.1437 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 1.6718 - val_loss: 0.3097 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 1.4871 - val_loss: 0.7577 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 1.7606 - val_loss: 1.2359 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 1.4735 - val_loss: 3.7849 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 1.1969 - val_loss: 0.5463 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 1.5938 - val_loss: 4.6894 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 127ms/step - loss: 1.7358 - val_loss: 1.6918 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 1.4060 - val_loss: 0.4342 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 13s 126ms/step - loss: 1.0667 - val_loss: 5.4104 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 128ms/step - loss: 3.5460 - val_loss: 1.2068 54/54 ━━━━━━━━━━━━━━━━━━━━ 2s 40ms/step Sample raw predictions (after inverse transform and clipping): [3.8448973 4.3981533 3.9058974 1.7887025 3.7183456] RMSE = 3.5128472 Validation R-squared for item 1282: -8.491267204284668 52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 38ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=23.944820404052734 True y_val range (after inverse transform): min=0.0, max=8.0
-----------------------------------
Current item is 2071
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=7
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.0
x_eval_time shape before reshape: (1637, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1917, 14)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (176, 20)
store_x_eval.shape: (171, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (236, 20)
store_x_eval.shape: (160, 20)
store_x_eval.shape: (205, 20)
store_x_eval.shape: (178, 20)
store_x_eval.shape: (221, 20)
Model: "sequential_157"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_315 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_220 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_316 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_221 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_156 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 134ms/step - loss: 17841.7148 - val_loss: 303.4991 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 1005.1638 - val_loss: 1.2742 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 41.4888 - val_loss: 0.4523 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 11.1480 - val_loss: 0.2274 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 4.7374 - val_loss: 0.0675 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 2.9514 - val_loss: 0.0230 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 1.9527 - val_loss: 0.5829 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 1.5042 - val_loss: 0.8634 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 1.7897 - val_loss: 0.6065 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 0.7277 - val_loss: 0.0100 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 0.7133 - val_loss: 0.0103 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 0.6434 - val_loss: 0.0424 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 0.4902 - val_loss: 0.2382 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 0.5096 - val_loss: 0.0566 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 0.4524 - val_loss: 0.0172 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 0.2683 - val_loss: 0.1337 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 0.4303 - val_loss: 0.0446 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 1.9005 - val_loss: 0.1519 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 0.7782 - val_loss: 0.1299 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 0.4152 - val_loss: 0.9068 52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 39ms/step Sample raw predictions (after inverse transform and clipping): [0.11523872 0.2424188 0.36801893 0.27300614 0.22022061] RMSE = 0.7506151 Validation R-squared for item 2071: -0.09216451644897461 52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 36ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=1.4117071628570557 True y_val range (after inverse transform): min=0.0, max=7.0
-----------------------------------
Current item is 1150
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=5
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.8
x_eval_time shape before reshape: (1623, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1903, 14)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (217, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (176, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (187, 20)
Model: "sequential_158"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_317 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_222 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_318 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_223 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_157 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 135ms/step - loss: 11214.9082 - val_loss: 284.7537 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 574.0168 - val_loss: 10.8568 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 135.6079 - val_loss: 8.9586 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 59.2822 - val_loss: 10.3243 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 26.6757 - val_loss: 2.1576 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 16.3684 - val_loss: 1.8340 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 8.9846 - val_loss: 1.2336 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 5.5760 - val_loss: 0.1891 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 3.4110 - val_loss: 0.8386 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 2.1602 - val_loss: 0.3900 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 1.6987 - val_loss: 0.4127 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 1.1926 - val_loss: 0.1861 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 139ms/step - loss: 0.7275 - val_loss: 0.1232 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 1.0564 - val_loss: 0.0968 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 0.7844 - val_loss: 0.0675 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 0.6671 - val_loss: 0.1604 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 1.8742 - val_loss: 0.1370 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 1.0914 - val_loss: 0.1966 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 1.2973 - val_loss: 0.3584 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 0.5718 - val_loss: 0.5125 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 0.5923 - val_loss: 0.1127 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 0.3244 - val_loss: 0.1197 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 0.2778 - val_loss: 0.0745 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 0.3903 - val_loss: 0.3648 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 0.2529 - val_loss: 0.1688 50/50 ━━━━━━━━━━━━━━━━━━━━ 2s 40ms/step Sample raw predictions (after inverse transform and clipping): [0. 0. 0. 0. 0.] RMSE = 0.6855278 Validation R-squared for item 1150: -0.6627413034439087 51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 38ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=13.094039916992188 True y_val range (after inverse transform): min=0.0, max=4.0
-----------------------------------
Current item is 2440
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=52
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.8076923076923077
x_eval_time shape before reshape: (1621, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1901, 14)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (169, 20)
store_x_eval.shape: (214, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (206, 20)
Model: "sequential_159"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_319 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_224 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_320 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_225 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_158 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 136ms/step - loss: 9610.1035 - val_loss: 82.6015 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 372.3842 - val_loss: 6.7885 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 73.6376 - val_loss: 3.2610 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 41.8971 - val_loss: 1.2933 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 23.0300 - val_loss: 0.5036 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 17.1519 - val_loss: 0.4794 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 10.2799 - val_loss: 0.1863 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 8.2328 - val_loss: 0.0860 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 6.0093 - val_loss: 0.0664 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 4.3283 - val_loss: 0.2106 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 4.5909 - val_loss: 0.0545 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 2.7136 - val_loss: 0.0518 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 3.1822 - val_loss: 0.1410 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 2.0756 - val_loss: 0.1270 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 1.7022 - val_loss: 0.1522 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 1.4829 - val_loss: 0.0653 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 1.2961 - val_loss: 0.0629 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 1.1700 - val_loss: 0.5049 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 1.0684 - val_loss: 0.1156 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 1.3937 - val_loss: 0.6218 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 137ms/step - loss: 5.8864 - val_loss: 0.0995 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 2.2710 - val_loss: 0.1379 52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 42ms/step Sample raw predictions (after inverse transform and clipping): [16.7485 11.687377 5.408379 10.92798 10.36963 ] RMSE = 14.551651 Validation R-squared for item 2440: -31.321369171142578 51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 39ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=273.25048828125 True y_val range (after inverse transform): min=0.0, max=28.000001907348633
-----------------------------------
Current item is 2275
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=10
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.5
x_eval_time shape before reshape: (1628, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1908, 14)
store_x_eval.shape: (210, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (215, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (174, 20)
store_x_eval.shape: (178, 20)
store_x_eval.shape: (180, 20)
store_x_eval.shape: (202, 20)
Model: "sequential_160"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_321 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_226 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_322 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_227 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_159 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 135ms/step - loss: 26791.0273 - val_loss: 29672.2324 Epoch 2/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 53993.0273 - val_loss: 513.0013 Epoch 3/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 673.9023 - val_loss: 31.1196 Epoch 4/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 162.3504 - val_loss: 29.2547 Epoch 5/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 114.7378 - val_loss: 15.9494 Epoch 6/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 100.4408 - val_loss: 22.4459 Epoch 7/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 69.6987 - val_loss: 13.6501 Epoch 8/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 53.8268 - val_loss: 13.0580 Epoch 9/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 47.1084 - val_loss: 11.2153 Epoch 10/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 39.7829 - val_loss: 11.0481 Epoch 11/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 33.9240 - val_loss: 13.1413 Epoch 12/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 31.8186 - val_loss: 8.8661 Epoch 13/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 28.8823 - val_loss: 6.2544 Epoch 14/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 24.9888 - val_loss: 5.6888 Epoch 15/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 22.7323 - val_loss: 7.8480 Epoch 16/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 21.5477 - val_loss: 5.6708 Epoch 17/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 18.8886 - val_loss: 5.5605 Epoch 18/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 17.1236 - val_loss: 7.0068 Epoch 19/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 16.3067 - val_loss: 9.2909 Epoch 20/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 15.0861 - val_loss: 5.3550 Epoch 21/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 13.8907 - val_loss: 3.4807 Epoch 22/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 11.8613 - val_loss: 5.2866 Epoch 23/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 11.5440 - val_loss: 2.2338 Epoch 24/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 10.4030 - val_loss: 2.3944 Epoch 25/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 9.9970 - val_loss: 2.4422 Epoch 26/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 8.9387 - val_loss: 1.7768 Epoch 27/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 8.0716 - val_loss: 1.6753 Epoch 28/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 7.2742 - val_loss: 2.3853 Epoch 29/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 7.0801 - val_loss: 3.3885 Epoch 30/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 6.9125 - val_loss: 6.3852 Epoch 31/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 6.3945 - val_loss: 5.4159 Epoch 32/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 6.9503 - val_loss: 1.1819 Epoch 33/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 5.2416 - val_loss: 5.1948 Epoch 34/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 5.0435 - val_loss: 2.6616 Epoch 35/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 4.9337 - val_loss: 4.0510 Epoch 36/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 4.9732 - val_loss: 0.8749 Epoch 37/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 3.9410 - val_loss: 1.0958 Epoch 38/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 3.5295 - val_loss: 0.4307 Epoch 39/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 3.1372 - val_loss: 1.4634 Epoch 40/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 3.2966 - val_loss: 0.6575 Epoch 41/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 2.8085 - val_loss: 2.1054 Epoch 42/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 3.0266 - val_loss: 0.9561 Epoch 43/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 2.3170 - val_loss: 1.6009 Epoch 44/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 2.7419 - val_loss: 1.3106 Epoch 45/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 1.8112 - val_loss: 0.2412 Epoch 46/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 1.9576 - val_loss: 4.4323 Epoch 47/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 2.1736 - val_loss: 1.0446 Epoch 48/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 2.0368 - val_loss: 0.1654 Epoch 49/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 2.3685 - val_loss: 1.3043 Epoch 50/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 1.6185 - val_loss: 0.6010 Epoch 51/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 1.2893 - val_loss: 0.1511 Epoch 52/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 1.1237 - val_loss: 0.1330 Epoch 53/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 23.8840 - val_loss: 0.2675 Epoch 54/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 1.0607 - val_loss: 0.5778 Epoch 55/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.8438 - val_loss: 0.2836 Epoch 56/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.9333 - val_loss: 0.1179 Epoch 57/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 1.7330 - val_loss: 0.0430 Epoch 58/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.6337 - val_loss: 0.0677 Epoch 59/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 1.4274 - val_loss: 0.0473 Epoch 60/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.7958 - val_loss: 0.5991 Epoch 61/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.5474 - val_loss: 0.1072 Epoch 62/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 2.0788 - val_loss: 0.1270 Epoch 63/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.4372 - val_loss: 0.0207 Epoch 64/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 1.2540 - val_loss: 0.0349 Epoch 65/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 2.0520 - val_loss: 1.5601 Epoch 66/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.3541 - val_loss: 0.0205 Epoch 67/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.5067 - val_loss: 1.8298 Epoch 68/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 132ms/step - loss: 0.5464 - val_loss: 0.8860 Epoch 69/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 2.4306 - val_loss: 0.3865 Epoch 70/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.2661 - val_loss: 2.1507 Epoch 71/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.9321 - val_loss: 0.0109 Epoch 72/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.3421 - val_loss: 0.3884 Epoch 73/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 2.1101 - val_loss: 0.1477 Epoch 74/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.6397 - val_loss: 2.3773 Epoch 75/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 1.9887 - val_loss: 0.2332 Epoch 76/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.5272 - val_loss: 0.6523 Epoch 77/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 3.8380 - val_loss: 0.0234 Epoch 78/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 1.9563 - val_loss: 0.0796 Epoch 79/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.0942 - val_loss: 0.0171 Epoch 80/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.0602 - val_loss: 0.0096 Epoch 81/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.0470 - val_loss: 0.0656 Epoch 82/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.3885 - val_loss: 0.0159 Epoch 83/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 1.4250 - val_loss: 0.5390 Epoch 84/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.1683 - val_loss: 0.1114 Epoch 85/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.0751 - val_loss: 0.0492 Epoch 86/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.0600 - val_loss: 0.2197 Epoch 87/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.1675 - val_loss: 0.4599 Epoch 88/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.6170 - val_loss: 0.1672 Epoch 89/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 1.5804 - val_loss: 0.0234 Epoch 90/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 131ms/step - loss: 0.3417 - val_loss: 0.0067 Epoch 91/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 0.4706 - val_loss: 0.0033 Epoch 92/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.2699 - val_loss: 2.4167 Epoch 93/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 2.0663 - val_loss: 0.0031 Epoch 94/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.1778 - val_loss: 0.0163 Epoch 95/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.7235 - val_loss: 0.3384 Epoch 96/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.1202 - val_loss: 0.0563 Epoch 97/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.2330 - val_loss: 0.0121 Epoch 98/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.1647 - val_loss: 0.0533 Epoch 99/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 130ms/step - loss: 42.2987 - val_loss: 0.0082 Epoch 100/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 14s 129ms/step - loss: 0.0202 - val_loss: 0.0060 50/50 ━━━━━━━━━━━━━━━━━━━━ 2s 40ms/step Sample raw predictions (after inverse transform and clipping): [0.41470802 0.27433136 0.35833877 0.45431012 0.24084675] RMSE = 0.67918676 Validation R-squared for item 2275: -0.08710885047912598 51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 37ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=0.7145441174507141 True y_val range (after inverse transform): min=0.0, max=5.0
-----------------------------------
Current item is 2493
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=5
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.0
x_eval_time shape before reshape: (1697, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1977, 14)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (205, 20)
store_x_eval.shape: (212, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (213, 20)
Model: "sequential_161"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_323 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_228 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_324 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_229 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_160 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 139ms/step - loss: 10158.6904 - val_loss: 958.8754 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 140ms/step - loss: 689.9169 - val_loss: 7.1501 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 139ms/step - loss: 44.1210 - val_loss: 2.0655 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 140ms/step - loss: 16.1113 - val_loss: 0.5033 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 139ms/step - loss: 9.5116 - val_loss: 0.2067 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 141ms/step - loss: 6.8276 - val_loss: 0.3904 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 139ms/step - loss: 4.6500 - val_loss: 0.1720 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 139ms/step - loss: 3.4991 - val_loss: 0.7321 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 142ms/step - loss: 2.6640 - val_loss: 0.0727 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 139ms/step - loss: 1.7941 - val_loss: 0.3509 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 139ms/step - loss: 1.0154 - val_loss: 0.0223 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 140ms/step - loss: 4.3788 - val_loss: 0.0262 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 139ms/step - loss: 0.6964 - val_loss: 0.0314 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 138ms/step - loss: 0.5533 - val_loss: 0.0584 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 141ms/step - loss: 0.4383 - val_loss: 0.0238 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 138ms/step - loss: 0.2738 - val_loss: 0.0783 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 140ms/step - loss: 0.1589 - val_loss: 0.0149 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 139ms/step - loss: 0.1620 - val_loss: 0.0114 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 140ms/step - loss: 0.1177 - val_loss: 0.0183 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 139ms/step - loss: 0.1248 - val_loss: 0.0338 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 140ms/step - loss: 0.1310 - val_loss: 0.0511 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 138ms/step - loss: 0.1507 - val_loss: 0.2860 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 140ms/step - loss: 0.1917 - val_loss: 24.3100 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 139ms/step - loss: 2.8185 - val_loss: 0.0176 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 139ms/step - loss: 0.1862 - val_loss: 0.0172 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 138ms/step - loss: 0.1457 - val_loss: 0.0383 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 139ms/step - loss: 0.0805 - val_loss: 0.0228 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 0.1175 - val_loss: 0.0913 50/50 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step Sample raw predictions (after inverse transform and clipping): [0.37514058 0.45209056 0.27968937 0.05441505 0.3064559 ] RMSE = 0.46462744 Validation R-squared for item 2493: -0.125604510307312 54/54 ━━━━━━━━━━━━━━━━━━━━ 2s 38ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=1.366655707359314 True y_val range (after inverse transform): min=0.0, max=5.0
-----------------------------------
Current item is 2295
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=10
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.8
x_eval_time shape before reshape: (1615, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1895, 14)
store_x_eval.shape: (207, 20)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (180, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (198, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (182, 20)
Model: "sequential_162"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_325 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_230 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_326 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_231 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_161 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 6719s 147ms/step - loss: 196283.9375 - val_loss: 23562.9629 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 142ms/step - loss: 81273.2812 - val_loss: 1188.6191 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 148ms/step - loss: 7540.7725 - val_loss: 1201.7190 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 145ms/step - loss: 2459.8171 - val_loss: 230.5009 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 148ms/step - loss: 1023.4954 - val_loss: 121.3973 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 741.0065 - val_loss: 82.3422 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 562.4069 - val_loss: 135.8063 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 464.3417 - val_loss: 53.0934 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 146ms/step - loss: 368.8252 - val_loss: 39.1010 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 299.8866 - val_loss: 32.9394 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 148ms/step - loss: 256.9548 - val_loss: 31.7178 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 216.2832 - val_loss: 25.6257 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 194.2388 - val_loss: 22.3754 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 159.1252 - val_loss: 16.0983 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 150ms/step - loss: 134.5797 - val_loss: 22.1327 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 149ms/step - loss: 118.8223 - val_loss: 19.1891 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 149ms/step - loss: 99.8904 - val_loss: 10.2108 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 89.4394 - val_loss: 8.3605 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 149ms/step - loss: 78.7821 - val_loss: 7.0579 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 149ms/step - loss: 66.6617 - val_loss: 7.3500 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 61.6112 - val_loss: 6.6963 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 56.3950 - val_loss: 5.2609 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 167ms/step - loss: 48.5325 - val_loss: 4.8061 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 167ms/step - loss: 45.0271 - val_loss: 9.0433 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 44.8252 - val_loss: 7.4727 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 38.7707 - val_loss: 2.6112 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 32.3414 - val_loss: 2.4760 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 28.7651 - val_loss: 2.4817 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 25.6937 - val_loss: 2.0706 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 23.3529 - val_loss: 1.7325 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 21.3614 - val_loss: 1.8803 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 20.4109 - val_loss: 1.5603 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 18.3791 - val_loss: 3.2624 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 17.3158 - val_loss: 4.4511 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 15.6975 - val_loss: 1.0363 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 13.8945 - val_loss: 1.3679 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 166ms/step - loss: 12.0633 - val_loss: 2.0575 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 12.0345 - val_loss: 1.3926 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 11.7324 - val_loss: 0.6213 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 9.3329 - val_loss: 2.9987 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 9.5137 - val_loss: 2.0247 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 8.7396 - val_loss: 3.8678 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 8.0577 - val_loss: 2.2001 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 6.8298 - val_loss: 2.9724 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 166ms/step - loss: 6.3415 - val_loss: 0.3758 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 7.0406 - val_loss: 3.4892 Epoch 47/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 5.2286 - val_loss: 0.3595 Epoch 48/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 4.8095 - val_loss: 0.8306 Epoch 49/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 4.3768 - val_loss: 1.0001 Epoch 50/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 3.9822 - val_loss: 0.2089 Epoch 51/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 164ms/step - loss: 3.6241 - val_loss: 0.6916 Epoch 52/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 167ms/step - loss: 3.6527 - val_loss: 0.3809 Epoch 53/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 3.9173 - val_loss: 0.5944 Epoch 54/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 3.6398 - val_loss: 0.1101 Epoch 55/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 2.7412 - val_loss: 0.1193 Epoch 56/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 2.7114 - val_loss: 0.1450 Epoch 57/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 2.4477 - val_loss: 0.4662 Epoch 58/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 1.9086 - val_loss: 1.3868 Epoch 59/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 2.5274 - val_loss: 1.4006 Epoch 60/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 2.2618 - val_loss: 1.7765 Epoch 61/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 2.0438 - val_loss: 0.0796 Epoch 62/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 2.3071 - val_loss: 1.0287 Epoch 63/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 4.1032 - val_loss: 0.0651 Epoch 64/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 1.6788 - val_loss: 1.1049 Epoch 65/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 1.6689 - val_loss: 3.5393 Epoch 66/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 3.8281 - val_loss: 0.4853 Epoch 67/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 1.4185 - val_loss: 4.2163 Epoch 68/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 2.1761 - val_loss: 0.0918 Epoch 69/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 1.4761 - val_loss: 0.0946 Epoch 70/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 2.2497 - val_loss: 0.1770 Epoch 71/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 1.2134 - val_loss: 0.0361 Epoch 72/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 0.9747 - val_loss: 0.2763 Epoch 73/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 1.5640 - val_loss: 0.8671 Epoch 74/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 62.5000 - val_loss: 0.7759 Epoch 75/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 3.9235 - val_loss: 0.0445 Epoch 76/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 2.4645 - val_loss: 0.0562 Epoch 77/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 0.4999 - val_loss: 0.0472 Epoch 78/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 0.4179 - val_loss: 0.0185 Epoch 79/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 0.3514 - val_loss: 0.0218 Epoch 80/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 0.3223 - val_loss: 0.0158 Epoch 81/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 0.2693 - val_loss: 0.0153 Epoch 82/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 141ms/step - loss: 0.3160 - val_loss: 0.1825 Epoch 83/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 138ms/step - loss: 0.3695 - val_loss: 0.0332 Epoch 84/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 138ms/step - loss: 0.2621 - val_loss: 0.1739 Epoch 85/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 138ms/step - loss: 0.2955 - val_loss: 0.0351 Epoch 86/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 138ms/step - loss: 0.2795 - val_loss: 0.3956 Epoch 87/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 139ms/step - loss: 0.3272 - val_loss: 0.0154 Epoch 88/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 0.2339 - val_loss: 0.0553 Epoch 89/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 135ms/step - loss: 0.2795 - val_loss: 0.0333 Epoch 90/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 0.2222 - val_loss: 0.0271 Epoch 91/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 0.1631 - val_loss: 0.0374 51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step Sample raw predictions (after inverse transform and clipping): [0. 0.5100306 0.51845336 0. 0. ] RMSE = 0.89877003 Validation R-squared for item 2295: -0.3055284023284912 51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 38ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=2.269831895828247 True y_val range (after inverse transform): min=0.0, max=8.0
-----------------------------------
Current item is 1405
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=5
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.0
x_eval_time shape before reshape: (1592, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1872, 14)
store_x_eval.shape: (170, 20)
store_x_eval.shape: (164, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (199, 20)
store_x_eval.shape: (171, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (203, 20)
Model: "sequential_163"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_327 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_232 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_328 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_233 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_162 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 139ms/step - loss: 41853.9102 - val_loss: 1703.3793 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 140ms/step - loss: 3186.4482 - val_loss: 172.7683 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 781.2379 - val_loss: 44.5581 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 391.5133 - val_loss: 51.9665 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 236.6488 - val_loss: 24.3202 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 163.3580 - val_loss: 16.4731 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 116.3193 - val_loss: 14.6715 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 141ms/step - loss: 94.3411 - val_loss: 13.1433 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 137ms/step - loss: 73.4092 - val_loss: 8.0270 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 137ms/step - loss: 60.3665 - val_loss: 6.3872 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 50.8782 - val_loss: 5.1146 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 137ms/step - loss: 39.9864 - val_loss: 14.0098 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 137ms/step - loss: 32.7865 - val_loss: 7.7327 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 25.9171 - val_loss: 6.9707 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 21.7164 - val_loss: 5.4468 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 18.7399 - val_loss: 6.6654 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 30.8758 - val_loss: 5.9538 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 18.2208 - val_loss: 7.4575 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 13.1441 - val_loss: 2.9795 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 137ms/step - loss: 9.1352 - val_loss: 2.8224 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 8.2573 - val_loss: 2.2899 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 138ms/step - loss: 6.3347 - val_loss: 3.2073 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 5.4501 - val_loss: 3.6204 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 4.3931 - val_loss: 2.0120 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 3.5196 - val_loss: 1.4187 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 2.8249 - val_loss: 1.5717 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 137ms/step - loss: 2.5675 - val_loss: 1.1250 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 2.0641 - val_loss: 0.9739 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 1.8870 - val_loss: 1.2474 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 1.6055 - val_loss: 0.5503 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 1.4476 - val_loss: 1.0856 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 1.2805 - val_loss: 0.4170 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 137ms/step - loss: 1.2055 - val_loss: 0.2133 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 1.3914 - val_loss: 0.3030 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 0.9816 - val_loss: 0.4450 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 137ms/step - loss: 1.0530 - val_loss: 0.1596 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 0.9507 - val_loss: 0.0810 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 137ms/step - loss: 0.7638 - val_loss: 0.5577 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 0.8065 - val_loss: 0.9310 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 137ms/step - loss: 0.9308 - val_loss: 0.1987 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 0.7339 - val_loss: 0.0630 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 0.6380 - val_loss: 0.2406 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 138ms/step - loss: 0.9683 - val_loss: 0.0444 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 0.5008 - val_loss: 2.4952 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 0.8973 - val_loss: 0.0383 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 0.7507 - val_loss: 0.2402 Epoch 47/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 0.8202 - val_loss: 0.2630 Epoch 48/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 0.5695 - val_loss: 0.5742 Epoch 49/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 0.7165 - val_loss: 1.9525 Epoch 50/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 0.9715 - val_loss: 0.1626 Epoch 51/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 1.3140 - val_loss: 0.0399 Epoch 52/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 0.3608 - val_loss: 0.1172 Epoch 53/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 0.5038 - val_loss: 2.3071 Epoch 54/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 137ms/step - loss: 1.1377 - val_loss: 1.1517 Epoch 55/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 137ms/step - loss: 1.2539 - val_loss: 0.4017 53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step Sample raw predictions (after inverse transform and clipping): [0. 0. 0.8184359 0.6945181 0. ] RMSE = 0.673594 Validation R-squared for item 1405: -1.0072171688079834 50/50 ━━━━━━━━━━━━━━━━━━━━ 2s 38ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=4.984849452972412 True y_val range (after inverse transform): min=0.0, max=5.0
-----------------------------------
Current item is 1013
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=20
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.8
x_eval_time shape before reshape: (1655, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1935, 14)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (176, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (177, 20)
store_x_eval.shape: (172, 20)
store_x_eval.shape: (237, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (198, 20)
Model: "sequential_164"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_329 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_234 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_330 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_235 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_163 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 136ms/step - loss: 48021.1875 - val_loss: 10569.5479 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 29628.4316 - val_loss: 422.1935 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 2437.9456 - val_loss: 90.5340 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 847.9769 - val_loss: 41.7940 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 429.5881 - val_loss: 18.7420 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 248.9054 - val_loss: 9.5722 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 138ms/step - loss: 184.9539 - val_loss: 6.0886 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 126.2957 - val_loss: 6.0139 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 93.3610 - val_loss: 6.9112 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 76.2517 - val_loss: 5.8735 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 60.3761 - val_loss: 4.1553 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 47.1582 - val_loss: 6.6050 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 38.9448 - val_loss: 4.3873 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 34.2571 - val_loss: 6.4738 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 29.7838 - val_loss: 1.5087 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 31.5257 - val_loss: 1.9249 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 22.1373 - val_loss: 2.9761 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 18.6961 - val_loss: 5.0042 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 18.5326 - val_loss: 1.8502 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 16.8063 - val_loss: 1.2883 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 16.8369 - val_loss: 1.4463 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 12.8103 - val_loss: 0.6492 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 11.4151 - val_loss: 0.8858 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 17.3360 - val_loss: 0.9642 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 7.0109 - val_loss: 0.8893 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 5.8840 - val_loss: 0.6980 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 6.3753 - val_loss: 0.3428 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 136ms/step - loss: 6.0170 - val_loss: 0.3516 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 6.0138 - val_loss: 0.4584 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 4.6010 - val_loss: 0.5501 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 4.9507 - val_loss: 0.2627 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 3.7938 - val_loss: 0.2145 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 3.7283 - val_loss: 0.4183 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 4.3152 - val_loss: 0.4099 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 3.1584 - val_loss: 0.1940 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 2.8501 - val_loss: 1.1303 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 3.6839 - val_loss: 0.1793 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 2.5758 - val_loss: 0.0955 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 2.8663 - val_loss: 0.0861 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 3.4693 - val_loss: 0.2041 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 1.5317 - val_loss: 0.0749 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 2.7122 - val_loss: 7.8611 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 33.3038 - val_loss: 4.6301 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 6.5292 - val_loss: 0.2875 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 4.3601 - val_loss: 0.2849 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 2.1717 - val_loss: 0.1102 Epoch 47/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 1.5541 - val_loss: 0.2558 Epoch 48/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 1.7726 - val_loss: 0.8000 Epoch 49/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 2.0804 - val_loss: 0.0835 Epoch 50/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 133ms/step - loss: 1.3844 - val_loss: 3.8364 Epoch 51/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 14s 134ms/step - loss: 2.3163 - val_loss: 1.0391 52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step Sample raw predictions (after inverse transform and clipping): [2.2213855 6.4484763 0.3758602 0. 0.36535102] RMSE = 4.832603 Validation R-squared for item 1013: -11.724756240844727 52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 38ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=54.19609069824219 True y_val range (after inverse transform): min=0.0, max=13.0
-----------------------------------
Current item is 1423
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=11
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.4545454545454546
x_eval_time shape before reshape: (1556, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1836, 14)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (170, 20)
store_x_eval.shape: (192, 20)
store_x_eval.shape: (173, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (205, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (173, 20)
Model: "sequential_165"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_331 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_236 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_332 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_237 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_164 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 144ms/step - loss: 26175.9883 - val_loss: 13301.8105 Epoch 2/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 140ms/step - loss: 10936.4199 - val_loss: 339.5075 Epoch 3/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 481.1048 - val_loss: 37.9467 Epoch 4/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 141ms/step - loss: 120.1431 - val_loss: 15.6336 Epoch 5/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 143.3273 - val_loss: 25.1780 Epoch 6/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 142ms/step - loss: 110.5352 - val_loss: 34.6851 Epoch 7/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 45.1754 - val_loss: 7.8212 Epoch 8/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 26.3397 - val_loss: 2.6428 Epoch 9/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 15.0817 - val_loss: 2.0156 Epoch 10/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 11.1229 - val_loss: 1.0675 Epoch 11/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 10.7054 - val_loss: 8.4704 Epoch 12/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 142ms/step - loss: 12.4444 - val_loss: 6.7464 Epoch 13/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 142ms/step - loss: 8.6343 - val_loss: 1.9086 Epoch 14/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 8.3899 - val_loss: 10.0375 Epoch 15/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 7.7643 - val_loss: 4.0236 Epoch 16/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 144ms/step - loss: 7.9887 - val_loss: 1.0783 Epoch 17/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 145ms/step - loss: 7.7469 - val_loss: 0.8902 Epoch 18/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 142ms/step - loss: 5.9835 - val_loss: 1.4161 Epoch 19/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 4.8943 - val_loss: 11.5499 Epoch 20/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 5.0112 - val_loss: 0.5473 Epoch 21/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 142ms/step - loss: 4.2177 - val_loss: 0.2626 Epoch 22/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 142ms/step - loss: 5.5435 - val_loss: 1.7304 Epoch 23/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 3.9700 - val_loss: 0.2638 Epoch 24/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 144ms/step - loss: 4.4083 - val_loss: 2.5410 Epoch 25/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 142ms/step - loss: 3.8710 - val_loss: 15.3921 Epoch 26/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 6.8947 - val_loss: 4.2897 Epoch 27/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 142ms/step - loss: 3.2120 - val_loss: 0.0985 Epoch 28/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 142ms/step - loss: 4.9776 - val_loss: 6.6287 Epoch 29/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 4.6643 - val_loss: 1.5703 Epoch 30/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 142ms/step - loss: 3.7327 - val_loss: 5.6929 Epoch 31/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 3.6115 - val_loss: 6.5618 Epoch 32/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 5.2497 - val_loss: 0.4556 Epoch 33/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 142ms/step - loss: 2.7280 - val_loss: 20.4897 Epoch 34/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 142ms/step - loss: 6.6911 - val_loss: 0.4609 Epoch 35/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 142ms/step - loss: 2.4141 - val_loss: 1.4230 Epoch 36/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 144ms/step - loss: 1.7808 - val_loss: 0.5163 Epoch 37/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 1.7477 - val_loss: 2.0559 52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 43ms/step Sample raw predictions (after inverse transform and clipping): [0. 2.2831542 0. 0. 0.14877482] RMSE = 2.3659296 Validation R-squared for item 1423: -28.13811492919922 49/49 ━━━━━━━━━━━━━━━━━━━━ 2s 40ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=13.75198745727539 True y_val range (after inverse transform): min=0.0, max=5.0
-----------------------------------
Current item is 2067
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=32
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.03125
x_eval_time shape before reshape: (1638, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1918, 14)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (177, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (210, 20)
store_x_eval.shape: (211, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (175, 20)
Model: "sequential_166"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_333 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_238 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_334 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_239 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_165 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 145ms/step - loss: 49758.1289 - val_loss: 4100.7563 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 31404.7949 - val_loss: 359.1188 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 2782.4688 - val_loss: 49.1615 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 932.6702 - val_loss: 23.3819 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 410.5623 - val_loss: 19.1208 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 141ms/step - loss: 217.5927 - val_loss: 15.0354 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 144ms/step - loss: 125.4185 - val_loss: 6.6713 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 144ms/step - loss: 90.3995 - val_loss: 5.8950 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 81.1977 - val_loss: 11.3496 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 144ms/step - loss: 75.9165 - val_loss: 12.2959 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 142ms/step - loss: 54.7328 - val_loss: 3.8332 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 38.8506 - val_loss: 2.3428 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 27.3894 - val_loss: 2.4105 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 144ms/step - loss: 21.1730 - val_loss: 1.7291 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 17.8600 - val_loss: 1.4781 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 13.2840 - val_loss: 0.8020 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 144ms/step - loss: 12.2094 - val_loss: 0.9192 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 145ms/step - loss: 9.9272 - val_loss: 3.1766 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 8.2222 - val_loss: 1.4799 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 7.1446 - val_loss: 2.3803 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 7.9671 - val_loss: 1.0391 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 39.6769 - val_loss: 0.4123 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 4.8794 - val_loss: 0.8115 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 4.8155 - val_loss: 0.3322 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 150ms/step - loss: 3.9087 - val_loss: 0.3938 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 3.3881 - val_loss: 0.3961 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 3.0871 - val_loss: 0.2012 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 146ms/step - loss: 2.8197 - val_loss: 0.1701 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 2.4934 - val_loss: 0.1227 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 2.0381 - val_loss: 0.5651 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 144ms/step - loss: 2.0195 - val_loss: 0.2722 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 149ms/step - loss: 1.5995 - val_loss: 0.5085 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 146ms/step - loss: 1.5628 - val_loss: 0.0709 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 150ms/step - loss: 1.3375 - val_loss: 0.2729 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 1.2282 - val_loss: 0.4542 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 1.1069 - val_loss: 0.0855 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 1.1198 - val_loss: 0.1040 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 1.0516 - val_loss: 0.0512 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.8213 - val_loss: 0.1911 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.8091 - val_loss: 0.0524 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.7329 - val_loss: 0.0475 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.5727 - val_loss: 0.0387 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.6283 - val_loss: 0.1047 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.5129 - val_loss: 0.2841 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.4641 - val_loss: 0.0223 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 0.5807 - val_loss: 0.1022 Epoch 47/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.3882 - val_loss: 0.0187 Epoch 48/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.3226 - val_loss: 0.0307 Epoch 49/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 155ms/step - loss: 0.3402 - val_loss: 0.0190 Epoch 50/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.3338 - val_loss: 0.0337 Epoch 51/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 0.2210 - val_loss: 0.0448 Epoch 52/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 149ms/step - loss: 0.2258 - val_loss: 0.0107 Epoch 53/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 144ms/step - loss: 0.1926 - val_loss: 0.1086 Epoch 54/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 144ms/step - loss: 0.2295 - val_loss: 0.0117 Epoch 55/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 146ms/step - loss: 0.2332 - val_loss: 0.0488 Epoch 56/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 145ms/step - loss: 0.3337 - val_loss: 0.0081 Epoch 57/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 144ms/step - loss: 0.1533 - val_loss: 0.0081 Epoch 58/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 144ms/step - loss: 0.1585 - val_loss: 0.0369 Epoch 59/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 145ms/step - loss: 0.1328 - val_loss: 0.0321 Epoch 60/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 146ms/step - loss: 0.1303 - val_loss: 0.0299 Epoch 61/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 145ms/step - loss: 2.1771 - val_loss: 0.0161 Epoch 62/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 15s 143ms/step - loss: 0.0920 - val_loss: 0.0225 Epoch 63/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 0.2446 - val_loss: 0.0201 Epoch 64/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 166ms/step - loss: 0.0889 - val_loss: 0.0060 Epoch 65/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 0.1404 - val_loss: 0.0067 Epoch 66/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 0.1306 - val_loss: 0.0015 Epoch 67/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.1145 - val_loss: 0.0056 Epoch 68/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.0889 - val_loss: 0.0434 Epoch 69/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.1677 - val_loss: 0.0363 Epoch 70/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 156ms/step - loss: 0.0551 - val_loss: 7.8802e-04 Epoch 71/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.1014 - val_loss: 0.0025 Epoch 72/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.0297 - val_loss: 0.0011 Epoch 73/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.0484 - val_loss: 0.0036 Epoch 74/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.1489 - val_loss: 0.0020 Epoch 75/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.0928 - val_loss: 0.0144 Epoch 76/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.0631 - val_loss: 0.0047 Epoch 77/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 0.1275 - val_loss: 0.0043 Epoch 78/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 0.0142 - val_loss: 0.0024 Epoch 79/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 0.0353 - val_loss: 0.0025 Epoch 80/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.0314 - val_loss: 0.0259 52/52 ━━━━━━━━━━━━━━━━━━━━ 3s 47ms/step Sample raw predictions (after inverse transform and clipping): [0. 0. 0.67165166 0.15849692 0.32690185] RMSE = 1.3307827 Validation R-squared for item 2067: -0.3342738151550293 52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 44ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=2.9452760219573975 True y_val range (after inverse transform): min=0.0, max=33.0
-----------------------------------
Current item is 2286
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=10
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.5
x_eval_time shape before reshape: (1579, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1859, 14)
store_x_eval.shape: (180, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (180, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (183, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (179, 20)
store_x_eval.shape: (165, 20)
Model: "sequential_167"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_335 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_240 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_336 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_241 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_166 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 19s 165ms/step - loss: 8558.5498 - val_loss: 63.7728 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 134.5322 - val_loss: 20.0135 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 30.3057 - val_loss: 9.8294 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 17.3734 - val_loss: 4.8284 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 11.0160 - val_loss: 1.3612 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 6.2297 - val_loss: 0.9176 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 4.5474 - val_loss: 0.5704 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 3.5358 - val_loss: 0.5308 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 2.4426 - val_loss: 0.2971 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 1.4475 - val_loss: 0.2013 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 1.0841 - val_loss: 0.1666 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.6682 - val_loss: 0.0908 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.5576 - val_loss: 0.0618 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.6013 - val_loss: 0.1127 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.2955 - val_loss: 0.0344 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.3005 - val_loss: 0.1153 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.2874 - val_loss: 0.0360 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.6016 - val_loss: 0.0137 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.2850 - val_loss: 0.0165 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.3283 - val_loss: 0.0262 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 165ms/step - loss: 0.2247 - val_loss: 0.0065 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 166ms/step - loss: 0.1603 - val_loss: 0.0246 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.1884 - val_loss: 0.1445 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.2328 - val_loss: 0.0050 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 164ms/step - loss: 0.1305 - val_loss: 0.1265 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.2113 - val_loss: 0.0203 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 172ms/step - loss: 0.1554 - val_loss: 0.0042 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.1030 - val_loss: 0.0352 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.1020 - val_loss: 0.0076 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.0879 - val_loss: 0.0035 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.0999 - val_loss: 0.1310 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 0.1749 - val_loss: 0.0553 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.1530 - val_loss: 0.0302 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.2074 - val_loss: 0.0106 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.1990 - val_loss: 0.0030 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.1055 - val_loss: 0.0023 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.0636 - val_loss: 0.0067 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 0.0645 - val_loss: 0.0366 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 0.0822 - val_loss: 0.0221 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.0877 - val_loss: 0.0037 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.0310 - val_loss: 0.0302 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.0365 - val_loss: 0.0118 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.0603 - val_loss: 0.0089 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 0.0975 - val_loss: 0.1860 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 164ms/step - loss: 0.4306 - val_loss: 0.0026 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 169ms/step - loss: 0.1040 - val_loss: 0.7465 52/52 ━━━━━━━━━━━━━━━━━━━━ 3s 46ms/step Sample raw predictions (after inverse transform and clipping): [0. 0. 0. 0. 0.] RMSE = 0.52935505 Validation R-squared for item 2286: -0.4546995162963867 50/50 ━━━━━━━━━━━━━━━━━━━━ 2s 44ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=8.565644264221191 True y_val range (after inverse transform): min=0.0, max=5.0
-----------------------------------
Current item is 2129
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=6
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.0
x_eval_time shape before reshape: (1636, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1916, 14)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (170, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (214, 20)
store_x_eval.shape: (164, 20)
store_x_eval.shape: (203, 20)
Model: "sequential_168"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_337 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_242 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_338 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_243 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_167 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 150ms/step - loss: 3730.8979 - val_loss: 43.3244 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 102.9349 - val_loss: 6.8940 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 29.9634 - val_loss: 0.7394 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 6.6715 - val_loss: 0.9451 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 6.0368 - val_loss: 0.2754 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 2.2330 - val_loss: 0.0953 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 1.1339 - val_loss: 0.0423 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.8631 - val_loss: 0.0169 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.5747 - val_loss: 0.0250 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.5116 - val_loss: 0.0209 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.3482 - val_loss: 0.0371 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.3904 - val_loss: 0.0459 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.3264 - val_loss: 0.0147 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 170ms/step - loss: 0.2669 - val_loss: 0.1919 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 172ms/step - loss: 0.1827 - val_loss: 0.0559 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.1396 - val_loss: 0.0311 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 167ms/step - loss: 0.1046 - val_loss: 0.0196 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.0844 - val_loss: 0.0059 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.0741 - val_loss: 0.0198 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.0730 - val_loss: 0.1273 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.1103 - val_loss: 0.0154 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.0567 - val_loss: 0.1748 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.0908 - val_loss: 0.0058 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.0768 - val_loss: 0.1383 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.0445 - val_loss: 0.0177 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.0339 - val_loss: 0.0132 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.0364 - val_loss: 0.0058 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 0.0596 - val_loss: 0.0084 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.0431 - val_loss: 0.0077 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.0363 - val_loss: 0.0210 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.0278 - val_loss: 0.0404 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.0427 - val_loss: 0.0240 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.0678 - val_loss: 0.0431 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.0278 - val_loss: 0.0317 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.0247 - val_loss: 0.0223 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.0275 - val_loss: 0.0054 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.0898 - val_loss: 0.0071 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.0184 - val_loss: 0.0219 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.0326 - val_loss: 0.0055 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.0129 - val_loss: 0.0093 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 0.0238 - val_loss: 0.0114 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.0238 - val_loss: 0.0267 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.0144 - val_loss: 0.0083 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.0199 - val_loss: 0.0253 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.0215 - val_loss: 0.0096 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.0135 - val_loss: 0.0117 52/52 ━━━━━━━━━━━━━━━━━━━━ 3s 47ms/step Sample raw predictions (after inverse transform and clipping): [0.12682389 0.14391771 0.13432156 0.08109893 0.03266741] RMSE = 0.49136835 Validation R-squared for item 2129: 0.022559702396392822 52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 43ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=0.37823107838630676 True y_val range (after inverse transform): min=0.0, max=6.0
-----------------------------------
Current item is 1082
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=15
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.9333333333333333
x_eval_time shape before reshape: (1637, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1917, 14)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (189, 20)
store_x_eval.shape: (202, 20)
store_x_eval.shape: (178, 20)
store_x_eval.shape: (165, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (194, 20)
Model: "sequential_169"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_339 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_244 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_340 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_245 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_168 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 156ms/step - loss: 22520.6328 - val_loss: 1209.0499 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 1057.8999 - val_loss: 120.9671 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 250.2575 - val_loss: 23.9698 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 66.1376 - val_loss: 2.2222 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 11.5387 - val_loss: 1.1044 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 5.6733 - val_loss: 0.5026 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 3.5553 - val_loss: 0.3384 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 2.7508 - val_loss: 0.2288 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 2.2894 - val_loss: 0.0988 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.9104 - val_loss: 0.3713 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 0.9204 - val_loss: 0.0776 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.7144 - val_loss: 0.0533 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 0.6194 - val_loss: 0.0510 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.5294 - val_loss: 0.1150 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.5974 - val_loss: 0.1812 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.3780 - val_loss: 0.1902 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.3830 - val_loss: 0.0934 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.2820 - val_loss: 0.0476 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 0.2773 - val_loss: 0.0890 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 0.2824 - val_loss: 0.1307 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.3207 - val_loss: 0.0485 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.2583 - val_loss: 0.2168 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.2167 - val_loss: 0.1284 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.1843 - val_loss: 0.3445 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 0.4737 - val_loss: 0.0180 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.2394 - val_loss: 0.0236 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.1223 - val_loss: 0.0569 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.1797 - val_loss: 0.0181 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.1550 - val_loss: 0.0278 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.1193 - val_loss: 0.0374 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.2710 - val_loss: 0.0136 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.0913 - val_loss: 0.0126 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.0488 - val_loss: 0.0371 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.0573 - val_loss: 0.0262 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 156ms/step - loss: 0.1713 - val_loss: 0.0228 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 0.0810 - val_loss: 0.1374 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 0.1062 - val_loss: 0.1152 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 0.0835 - val_loss: 0.0737 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 0.0861 - val_loss: 0.1326 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 0.1285 - val_loss: 0.1604 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.1029 - val_loss: 0.0760 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.1219 - val_loss: 0.0192 51/51 ━━━━━━━━━━━━━━━━━━━━ 3s 47ms/step Sample raw predictions (after inverse transform and clipping): [1.612508 1.4084543 0.5608609 1.3006264 0.14584604] RMSE = 1.698187 Validation R-squared for item 1082: -0.18663406372070312 52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 43ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=6.648131847381592 True y_val range (after inverse transform): min=0.0, max=14.0
-----------------------------------
Current item is 1254
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=57
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.631578947368421
x_eval_time shape before reshape: (1610, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1890, 14)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (169, 20)
store_x_eval.shape: (206, 20)
store_x_eval.shape: (188, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (168, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (199, 20)
Model: "sequential_170"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_341 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_246 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_342 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_247 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_169 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 19s 161ms/step - loss: 15933.2725 - val_loss: 941.1097 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 4106.5435 - val_loss: 67.1737 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 496.1465 - val_loss: 18.8257 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 175.1661 - val_loss: 13.0893 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 83.0492 - val_loss: 7.7522 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 166ms/step - loss: 47.6428 - val_loss: 4.6468 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 23.6646 - val_loss: 2.5897 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 15.2227 - val_loss: 1.2845 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 14.9872 - val_loss: 4.7233 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 13.0479 - val_loss: 4.2775 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 164ms/step - loss: 13.6110 - val_loss: 1.1205 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 164ms/step - loss: 6.4176 - val_loss: 0.9462 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 166ms/step - loss: 4.0936 - val_loss: 0.4405 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 2.9331 - val_loss: 0.5176 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 2.2207 - val_loss: 0.3241 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 1.8981 - val_loss: 0.8755 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 1.5066 - val_loss: 0.2130 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 164ms/step - loss: 1.6624 - val_loss: 0.4517 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 164ms/step - loss: 1.0594 - val_loss: 0.2588 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 166ms/step - loss: 1.0668 - val_loss: 0.6110 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 169ms/step - loss: 0.9924 - val_loss: 0.1931 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 169ms/step - loss: 0.9408 - val_loss: 2.1674 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.9311 - val_loss: 0.6126 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 164ms/step - loss: 0.7298 - val_loss: 0.1147 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 164ms/step - loss: 0.6686 - val_loss: 0.0926 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.5966 - val_loss: 0.0931 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.7041 - val_loss: 1.0019 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 164ms/step - loss: 0.9191 - val_loss: 0.0438 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.4458 - val_loss: 0.3220 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 164ms/step - loss: 0.5400 - val_loss: 0.2016 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.3742 - val_loss: 0.0333 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.3590 - val_loss: 0.4395 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.3602 - val_loss: 0.1078 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.3841 - val_loss: 0.0225 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.2899 - val_loss: 0.0278 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.2120 - val_loss: 1.2044 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.4807 - val_loss: 0.0838 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.1812 - val_loss: 0.4819 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.5412 - val_loss: 0.0161 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.1966 - val_loss: 0.4338 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.5745 - val_loss: 0.1091 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.1817 - val_loss: 0.0155 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.1565 - val_loss: 0.0110 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.1621 - val_loss: 1.0604 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.3572 - val_loss: 0.7474 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 0.2541 - val_loss: 0.1619 Epoch 47/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 0.1451 - val_loss: 0.0151 Epoch 48/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.4166 - val_loss: 0.0084 Epoch 49/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.2506 - val_loss: 0.0565 Epoch 50/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.1418 - val_loss: 0.0323 Epoch 51/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 150ms/step - loss: 0.1150 - val_loss: 1.2632 Epoch 52/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 0.3109 - val_loss: 0.0096 Epoch 53/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.1934 - val_loss: 0.1209 Epoch 54/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.1930 - val_loss: 0.1396 Epoch 55/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.0969 - val_loss: 0.0852 Epoch 56/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.0829 - val_loss: 0.0227 Epoch 57/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.1642 - val_loss: 0.0157 Epoch 58/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.1037 - val_loss: 0.0111 52/52 ━━━━━━━━━━━━━━━━━━━━ 3s 49ms/step Sample raw predictions (after inverse transform and clipping): [ 4.533003 3.0275521 10.059122 2.091683 7.482409 ] RMSE = 5.100913 Validation R-squared for item 1254: -0.1990525722503662 51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 43ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=18.20879364013672 True y_val range (after inverse transform): min=0.0, max=36.0
-----------------------------------
Current item is 2257
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=10
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.6000000000000001
x_eval_time shape before reshape: (1642, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1922, 14)
store_x_eval.shape: (174, 20)
store_x_eval.shape: (185, 20)
store_x_eval.shape: (215, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (186, 20)
store_x_eval.shape: (171, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (209, 20)
store_x_eval.shape: (201, 20)
store_x_eval.shape: (179, 20)
Model: "sequential_171"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_343 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_248 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_344 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_249 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_170 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 20s 168ms/step - loss: 7655.8535 - val_loss: 780.7281 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 698.8498 - val_loss: 8.7090 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 41.9981 - val_loss: 2.2838 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 19.0797 - val_loss: 1.8406 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 12.3102 - val_loss: 0.7391 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 6.7863 - val_loss: 0.5995 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 5.6349 - val_loss: 0.3972 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 3.8613 - val_loss: 0.5598 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 2.5730 - val_loss: 0.2595 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 2.3194 - val_loss: 0.1624 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 1.8419 - val_loss: 0.2326 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 1.3443 - val_loss: 0.1067 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 1.2257 - val_loss: 0.0792 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.7111 - val_loss: 0.3300 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.8048 - val_loss: 0.1224 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.4845 - val_loss: 0.0704 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.7364 - val_loss: 0.1918 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 2.0593 - val_loss: 0.1270 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.7509 - val_loss: 0.0614 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.5360 - val_loss: 0.4027 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 0.4971 - val_loss: 0.2346 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 0.4761 - val_loss: 0.2699 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.4675 - val_loss: 0.0732 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 0.3231 - val_loss: 0.0496 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.2880 - val_loss: 0.1316 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.4163 - val_loss: 0.0475 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.3311 - val_loss: 0.0918 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.2660 - val_loss: 0.0283 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 0.2550 - val_loss: 0.1645 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.5115 - val_loss: 0.0430 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 0.2554 - val_loss: 0.0264 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.1502 - val_loss: 0.0107 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.2469 - val_loss: 0.0218 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.1570 - val_loss: 0.0240 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.1391 - val_loss: 0.0875 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 0.1865 - val_loss: 0.0331 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.1772 - val_loss: 0.0109 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 0.1245 - val_loss: 0.0426 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.2395 - val_loss: 0.4324 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 0.2072 - val_loss: 0.0731 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.1419 - val_loss: 0.0205 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 0.2081 - val_loss: 0.0794 51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step Sample raw predictions (after inverse transform and clipping): [1.0130391 0.5801868 0.42985553 0.3100687 0.19320995] RMSE = 1.0275563 Validation R-squared for item 2257: -0.4919395446777344 52/52 ━━━━━━━━━━━━━━━━━━━━ 2s 43ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=15.472684860229492 True y_val range (after inverse transform): min=0.0, max=6.0
-----------------------------------
Current item is 2226
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=7
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.7142857142857142
x_eval_time shape before reshape: (1670, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1950, 14)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (204, 20)
store_x_eval.shape: (213, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (164, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (202, 20)
Model: "sequential_172"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_345 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_250 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_346 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_251 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_171 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 18s 152ms/step - loss: 19788.1855 - val_loss: 274.8459 Epoch 2/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 1005.2773 - val_loss: 42.6862 Epoch 3/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 296.0948 - val_loss: 24.6790 Epoch 4/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 166.2050 - val_loss: 12.2961 Epoch 5/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 109.4815 - val_loss: 5.9555 Epoch 6/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 68.3742 - val_loss: 4.3653 Epoch 7/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 43.0775 - val_loss: 1.7519 Epoch 8/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 150ms/step - loss: 31.2232 - val_loss: 3.2773 Epoch 9/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 150ms/step - loss: 23.2122 - val_loss: 2.0105 Epoch 10/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 23.7326 - val_loss: 1.4713 Epoch 11/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 150ms/step - loss: 21.1586 - val_loss: 0.9883 Epoch 12/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 14.4843 - val_loss: 0.6904 Epoch 13/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 10.4123 - val_loss: 0.2270 Epoch 14/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 4.9396 - val_loss: 0.4499 Epoch 15/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 3.4180 - val_loss: 0.1340 Epoch 16/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 2.9707 - val_loss: 0.1373 Epoch 17/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 1.8369 - val_loss: 0.2318 Epoch 18/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 154ms/step - loss: 1.6513 - val_loss: 0.1853 Epoch 19/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 150ms/step - loss: 1.2508 - val_loss: 0.2720 Epoch 20/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 1.0896 - val_loss: 0.0603 Epoch 21/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 0.9523 - val_loss: 0.0671 Epoch 22/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 150ms/step - loss: 0.9589 - val_loss: 0.0534 Epoch 23/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 2.7293 - val_loss: 0.6142 Epoch 24/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 150ms/step - loss: 4.1293 - val_loss: 0.4313 Epoch 25/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 2.1810 - val_loss: 0.3205 Epoch 26/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 0.9458 - val_loss: 0.6609 Epoch 27/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 150ms/step - loss: 0.6357 - val_loss: 0.0428 Epoch 28/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 150ms/step - loss: 0.6377 - val_loss: 0.0426 Epoch 29/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 0.3435 - val_loss: 5.8038 Epoch 30/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 2.0712 - val_loss: 0.0968 Epoch 31/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 0.2795 - val_loss: 0.0191 Epoch 32/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 150ms/step - loss: 0.2409 - val_loss: 0.0169 Epoch 33/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 0.1859 - val_loss: 0.2902 Epoch 34/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 0.3451 - val_loss: 0.0753 Epoch 35/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 0.1169 - val_loss: 0.0174 Epoch 36/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.1819 - val_loss: 0.1132 Epoch 37/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.2691 - val_loss: 0.0608 Epoch 38/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 1.1690 - val_loss: 0.1198 Epoch 39/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 0.3410 - val_loss: 0.0618 Epoch 40/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 1.4134 - val_loss: 0.0743 Epoch 41/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 9.3544 - val_loss: 719.4838 Epoch 42/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 149ms/step - loss: 139.1706 - val_loss: 0.3270 47/47 ━━━━━━━━━━━━━━━━━━━━ 2s 46ms/step Sample raw predictions (after inverse transform and clipping): [0.41771007 1.195912 0.9953283 0.91815364 1.08146 ] RMSE = 0.8685413 Validation R-squared for item 2226: -0.8839174509048462 53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 42ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=3.5996005535125732 True y_val range (after inverse transform): min=0.0, max=5.0
-----------------------------------
Current item is 1283
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=28
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.46428571428571425
x_eval_time shape before reshape: (1604, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1884, 14)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (182, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (196, 20)
store_x_eval.shape: (181, 20)
store_x_eval.shape: (213, 20)
store_x_eval.shape: (176, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (183, 20)
Model: "sequential_173"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_347 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_252 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_348 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_253 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_172 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 146ms/step - loss: 2103.4707 - val_loss: 13.8779 Epoch 2/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 77.4016 - val_loss: 3.1525 Epoch 3/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 149ms/step - loss: 8.4946 - val_loss: 0.2621 Epoch 4/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 150ms/step - loss: 1.2050 - val_loss: 0.2581 Epoch 5/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 145ms/step - loss: 0.5822 - val_loss: 0.0142 Epoch 6/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 144ms/step - loss: 0.2352 - val_loss: 0.2116 Epoch 7/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 15s 144ms/step - loss: 0.2359 - val_loss: 0.0146 Epoch 8/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 149ms/step - loss: 0.1990 - val_loss: 0.0643 Epoch 9/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 155ms/step - loss: 0.1490 - val_loss: 0.0518 Epoch 10/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 154ms/step - loss: 0.1222 - val_loss: 0.0526 Epoch 11/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.1653 - val_loss: 0.0120 Epoch 12/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.0974 - val_loss: 0.2396 Epoch 13/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.0988 - val_loss: 0.0058 Epoch 14/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.1241 - val_loss: 0.1660 Epoch 15/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 0.1062 - val_loss: 0.2396 Epoch 16/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 155ms/step - loss: 0.1304 - val_loss: 0.0111 Epoch 17/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.0658 - val_loss: 0.1949 Epoch 18/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 155ms/step - loss: 0.0856 - val_loss: 0.1554 Epoch 19/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 0.1312 - val_loss: 0.0049 Epoch 20/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.0549 - val_loss: 0.1113 Epoch 21/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 0.0634 - val_loss: 0.0460 Epoch 22/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 146ms/step - loss: 0.0605 - val_loss: 0.1674 Epoch 23/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 0.0734 - val_loss: 0.1554 Epoch 24/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.0830 - val_loss: 0.6487 Epoch 25/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 0.1663 - val_loss: 0.0692 Epoch 26/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.0616 - val_loss: 0.0042 Epoch 27/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.0727 - val_loss: 0.2024 Epoch 28/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 0.1037 - val_loss: 0.0279 Epoch 29/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 0.0304 - val_loss: 0.2230 Epoch 30/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 149ms/step - loss: 0.1096 - val_loss: 0.0072 Epoch 31/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 0.0354 - val_loss: 0.2190 Epoch 32/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 146ms/step - loss: 0.0881 - val_loss: 0.0274 Epoch 33/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 0.0598 - val_loss: 0.0046 Epoch 34/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 0.0370 - val_loss: 0.6927 Epoch 35/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 0.4623 - val_loss: 0.0037 Epoch 36/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 0.0153 - val_loss: 0.0721 Epoch 37/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 0.0244 - val_loss: 0.0059 Epoch 38/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 0.0316 - val_loss: 0.0050 Epoch 39/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 146ms/step - loss: 0.1076 - val_loss: 0.0626 Epoch 40/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 0.0313 - val_loss: 0.0049 Epoch 41/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 0.0164 - val_loss: 0.1659 Epoch 42/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 147ms/step - loss: 0.0960 - val_loss: 0.0095 Epoch 43/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 146ms/step - loss: 0.0273 - val_loss: 0.0140 Epoch 44/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 148ms/step - loss: 0.0449 - val_loss: 0.0110 Epoch 45/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 146ms/step - loss: 0.0600 - val_loss: 0.0119 50/50 ━━━━━━━━━━━━━━━━━━━━ 2s 44ms/step Sample raw predictions (after inverse transform and clipping): [0.3720941 0.5416374 0.835038 1.0700347 0.78572196] RMSE = 1.7831588 Validation R-squared for item 1283: -0.011059045791625977 51/51 ━━━━━━━━━━━━━━━━━━━━ 2s 41ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=2.3024468421936035 True y_val range (after inverse transform): min=0.0, max=13.0
-----------------------------------
Current item is 2141
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=3
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=1.0
x_eval_time shape before reshape: (1575, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1855, 14)
store_x_eval.shape: (197, 20)
store_x_eval.shape: (191, 20)
store_x_eval.shape: (184, 20)
store_x_eval.shape: (190, 20)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (163, 20)
store_x_eval.shape: (187, 20)
store_x_eval.shape: (200, 20)
store_x_eval.shape: (175, 20)
store_x_eval.shape: (175, 20)
Model: "sequential_174"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_349 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_254 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_350 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_255 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_173 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 18s 154ms/step - loss: 9047.7363 - val_loss: 252.6134 Epoch 2/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 452.2170 - val_loss: 10.1615 Epoch 3/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 37.2436 - val_loss: 1.7122 Epoch 4/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 8.6455 - val_loss: 0.7565 Epoch 5/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 4.2641 - val_loss: 0.3718 Epoch 6/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 2.4616 - val_loss: 1.3413 Epoch 7/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 159ms/step - loss: 1.9163 - val_loss: 0.0338 Epoch 8/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.8954 - val_loss: 0.0559 Epoch 9/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 154ms/step - loss: 0.4216 - val_loss: 0.0259 Epoch 10/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 156ms/step - loss: 0.2358 - val_loss: 0.0094 Epoch 11/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 0.2857 - val_loss: 0.1292 Epoch 12/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.2143 - val_loss: 0.0330 Epoch 13/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.0881 - val_loss: 0.1087 Epoch 14/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.1456 - val_loss: 0.0212 Epoch 15/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.1264 - val_loss: 0.1176 Epoch 16/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.1320 - val_loss: 0.1188 Epoch 17/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 0.1049 - val_loss: 0.0331 Epoch 18/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 150ms/step - loss: 0.0993 - val_loss: 0.3805 Epoch 19/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 150ms/step - loss: 0.2172 - val_loss: 0.0158 Epoch 20/100 107/107 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.1209 - val_loss: 0.1267 51/51 ━━━━━━━━━━━━━━━━━━━━ 3s 46ms/step Sample raw predictions (after inverse transform and clipping): [0.1080399 0.19204085 0.14207019 0.09809212 0.10098216] RMSE = 0.31334543 Validation R-squared for item 2141: -0.19822299480438232 50/50 ━━━━━━━━━━━━━━━━━━━━ 2s 42ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=1.939897060394287 True y_val range (after inverse transform): min=0.0, max=3.0
-----------------------------------
Current item is 2501
Data types of train_dummy after one-hot encoding:
id object
item_id object
dept_id object
d object
num_sold int64
wm_yr_wk int64
month int64
year int64
sell_price float64
event_type float64
store_id_1 bool
store_id_2 bool
store_id_3 bool
store_id_4 bool
state_id_1 bool
state_id_2 bool
state_id_3 bool
weekday_1 bool
weekday_2 bool
weekday_3 bool
weekday_4 bool
weekday_5 bool
weekday_6 bool
weekday_7 bool
snap_0 bool
snap_1 bool
dtype: object
Index(['d', 'month', 'sell_price', 'event_type', 'store_id_1', 'store_id_2',
'store_id_3', 'store_id_4', 'state_id_1', 'state_id_2', 'state_id_3',
'weekday_1', 'weekday_2', 'weekday_3', 'weekday_4', 'weekday_5',
'weekday_6', 'weekday_7', 'snap_0', 'snap_1'],
dtype='object')
Original y_data range: min=0, max=5
Scaled y_data range: min=0.0, max=1.0
Scaled y_val range: min=0.0, max=0.8
x_eval_time shape before reshape: (1675, 15, 20)
x_data.shape[1]: 20
LOOKBACK_ARR.shape[0]: 15
item_eval_data.shape: (1955, 14)
store_x_eval.shape: (193, 20)
store_x_eval.shape: (208, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (207, 20)
store_x_eval.shape: (215, 20)
store_x_eval.shape: (203, 20)
store_x_eval.shape: (195, 20)
store_x_eval.shape: (194, 20)
store_x_eval.shape: (156, 20)
store_x_eval.shape: (190, 20)
Model: "sequential_175"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩ │ lstm_351 (LSTM) │ (None, 15, 256) │ 283,648 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_256 (Dropout) │ (None, 15, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ lstm_352 (LSTM) │ (None, 256) │ 525,312 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dropout_257 (Dropout) │ (None, 256) │ 0 │ ├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤ │ dense_174 (Dense) │ (None, 1) │ 257 │ └──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
Total params: 809,217 (3.09 MB)
Trainable params: 809,217 (3.09 MB)
Non-trainable params: 0 (0.00 B)
Epoch 1/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 156ms/step - loss: 15058.1328 - val_loss: 583.1071 Epoch 2/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 151ms/step - loss: 1731.4601 - val_loss: 13.6283 Epoch 3/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 182.6647 - val_loss: 58.2110 Epoch 4/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 91.0743 - val_loss: 19.8143 Epoch 5/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 58.1783 - val_loss: 23.0297 Epoch 6/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 35.0219 - val_loss: 10.2392 Epoch 7/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 25.0402 - val_loss: 3.9456 Epoch 8/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 17.6153 - val_loss: 2.8852 Epoch 9/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 13.1647 - val_loss: 3.6694 Epoch 10/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 9.9337 - val_loss: 2.1119 Epoch 11/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 9.3490 - val_loss: 0.6402 Epoch 12/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 7.2781 - val_loss: 1.6011 Epoch 13/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 5.1549 - val_loss: 0.7208 Epoch 14/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 4.6636 - val_loss: 2.0179 Epoch 15/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 4.0433 - val_loss: 1.7149 Epoch 16/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 3.3420 - val_loss: 0.6422 Epoch 17/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 2.9785 - val_loss: 0.6802 Epoch 18/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 2.6686 - val_loss: 0.2398 Epoch 19/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 2.0525 - val_loss: 0.7862 Epoch 20/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 1.7755 - val_loss: 1.1510 Epoch 21/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 1.8622 - val_loss: 0.4077 Epoch 22/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 1.3652 - val_loss: 0.2420 Epoch 23/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 1.8130 - val_loss: 0.1873 Epoch 24/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 1.0085 - val_loss: 0.2320 Epoch 25/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 0.9697 - val_loss: 0.2792 Epoch 26/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.8874 - val_loss: 0.3049 Epoch 27/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 157ms/step - loss: 0.7864 - val_loss: 0.0552 Epoch 28/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.9243 - val_loss: 0.0968 Epoch 29/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 153ms/step - loss: 0.8286 - val_loss: 0.0412 Epoch 30/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 152ms/step - loss: 0.5233 - val_loss: 0.1569 Epoch 31/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 0.5010 - val_loss: 0.1768 Epoch 32/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 154ms/step - loss: 0.3530 - val_loss: 0.0815 Epoch 33/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 0.3238 - val_loss: 0.0204 Epoch 34/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.4121 - val_loss: 0.1508 Epoch 35/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 16s 155ms/step - loss: 0.4789 - val_loss: 0.1080 Epoch 36/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 158ms/step - loss: 0.2949 - val_loss: 0.0520 Epoch 37/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 169ms/step - loss: 0.2134 - val_loss: 0.0387 Epoch 38/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 166ms/step - loss: 0.3290 - val_loss: 0.0337 Epoch 39/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.2829 - val_loss: 0.0164 Epoch 40/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 1.5624 - val_loss: 0.1792 Epoch 41/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.3379 - val_loss: 0.0181 Epoch 42/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.2724 - val_loss: 0.0521 Epoch 43/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.1437 - val_loss: 0.0136 Epoch 44/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.0934 - val_loss: 0.0155 Epoch 45/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.1282 - val_loss: 0.0135 Epoch 46/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.1528 - val_loss: 0.0383 Epoch 47/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.0777 - val_loss: 0.0201 Epoch 48/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.0751 - val_loss: 0.0801 Epoch 49/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.0832 - val_loss: 0.0945 Epoch 50/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.1012 - val_loss: 0.1727 Epoch 51/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.1368 - val_loss: 0.0126 Epoch 52/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.0628 - val_loss: 0.0977 Epoch 53/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 164ms/step - loss: 0.0811 - val_loss: 0.0164 Epoch 54/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.0467 - val_loss: 0.2808 Epoch 55/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.1174 - val_loss: 0.1855 Epoch 56/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.1716 - val_loss: 0.0173 Epoch 57/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.0572 - val_loss: 0.0937 Epoch 58/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.1338 - val_loss: 0.0278 Epoch 59/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.0759 - val_loss: 0.0452 Epoch 60/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 165ms/step - loss: 0.0722 - val_loss: 0.0126 Epoch 61/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 18s 166ms/step - loss: 0.1084 - val_loss: 0.0143 Epoch 62/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 164ms/step - loss: 0.0367 - val_loss: 0.0145 Epoch 63/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.0761 - val_loss: 0.0124 Epoch 64/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.0377 - val_loss: 0.0439 Epoch 65/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.0676 - val_loss: 0.0476 Epoch 66/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.0875 - val_loss: 0.0173 Epoch 67/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 160ms/step - loss: 0.0408 - val_loss: 0.0185 Epoch 68/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.0621 - val_loss: 0.0148 Epoch 69/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 161ms/step - loss: 0.0259 - val_loss: 0.0161 Epoch 70/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.0430 - val_loss: 0.0131 Epoch 71/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 162ms/step - loss: 0.0957 - val_loss: 0.0468 Epoch 72/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.0923 - val_loss: 0.0295 Epoch 73/100 106/106 ━━━━━━━━━━━━━━━━━━━━ 17s 163ms/step - loss: 0.4627 - val_loss: 1.0215 51/51 ━━━━━━━━━━━━━━━━━━━━ 3s 48ms/step Sample raw predictions (after inverse transform and clipping): [0.24653293 0.3108156 0.4152503 0.47571957 0.43431592] RMSE = 0.54304177 Validation R-squared for item 2501: 0.011475861072540283 53/53 ━━━━━━━━━━━━━━━━━━━━ 2s 44ms/step Predicted y_val range (after inverse transform and clipping): min=0.0, max=0.6765174865722656 True y_val range (after inverse transform): min=0.0, max=4.0
----------------------------------- Overall Validation MSE: 17.821836 Overall Validation RMSE: 4.2215915 Overall Validation MAE: 1.3745992 Overall Validation R-squared: -2.6647260009051483
In [41]:
y_val_pre_all = np.array(y_val_pre_all, dtype=object)
y_eval_pre_all = np.array(y_eval_pre_all, dtype=object)
def truncate_to_divisible(arr, divisor=28):
if isinstance(arr, (list, np.ndarray)):
current_size = len(arr)
new_size = current_size - (current_size % divisor)
return arr[:new_size]
else:
print(f"Warning: Skipping scalar element: {arr}")
return np.array([]) # Return an empty array or handle as needed
y_val_output = [truncate_to_divisible(x).reshape(-1, 28) for x in y_val_pre_all if isinstance(x, (list, np.ndarray))]
y_eval_output = [truncate_to_divisible(x).reshape(-1, 28) for x in y_eval_pre_all if isinstance(x, (list, np.ndarray))]
# Check shapes
for i, arr in enumerate(y_val_output[:5]):
print(f"Array {i}: shape {arr.shape}")
In [42]:
val_id_all = np.array(val_id_all)
eval_id_all = np.array(eval_id_all)
val_id_output = val_id_all.reshape(val_id_all.shape[0]*val_id_all.shape[1])
eval_id_output = eval_id_all.reshape(eval_id_all.shape[0]*eval_id_all.shape[1])
print(val_id_output.shape, eval_id_output.shape)
(1000,) (1000,)
In [43]:
y_val_output = [arr.flatten() for arr in y_val_output]
max_length = max(len(arr) for arr in y_val_output)
padded = [np.pad(arr, (0, max_length - len(arr)), 'constant') for arr in y_val_output]
y_val_output = np.vstack(padded)
if y_val_output.shape[1] % 28 != 0:
padded_length = ((y_val_output.shape[1] + 27) // 28) * 28
y_val_output = np.pad(y_val_output, ((0, 0), (0, padded_length - y_val_output.shape[1])), 'constant')
y_val_output = y_val_output.reshape(-1, 28)
num_samples = y_val_output.shape[0]
if len(val_id_output) > num_samples:
val_id_output = val_id_output[:num_samples]
elif len(val_id_output) < num_samples:
val_id_output = np.concatenate([val_id_output, np.full(num_samples - len(val_id_output), np.nan)])
output_cols = ['id'] + [f'F{i+1}' for i in range(28)]
val_df = pd.DataFrame(
{'id': val_id_output},
columns=['id'] + [f'F{i+1}' for i in range(28)]
)
for i in range(28):
val_df[f'F{i+1}'] = y_val_output[:, i]
print(val_df.head())
print(f"Final shape: {val_df.shape}")
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) Cell In[43], line 4 1 y_val_output = [arr.flatten() for arr in y_val_output] 3 # 2. Pad all arrays to max length ----> 4 max_length = max(len(arr) for arr in y_val_output) 5 padded = [np.pad(arr, (0, max_length - len(arr)), 'constant') for arr in y_val_output] 7 # 3. Stack into 2D array ValueError: max() iterable argument is empty
In [106]:
y_eval_output = [np.array(arr).flatten() for arr in y_eval_output]
max_length = 0
if y_eval_output:
max_length = max(len(arr) for arr in y_eval_output)
padded = [np.pad(arr, (0, max_length - len(arr)), 'constant') for arr in y_eval_output]
y_eval_output = np.vstack(padded) if padded else np.empty((0, 0))
if y_eval_output.shape[1] < 28:
y_eval_output = np.pad(y_eval_output, ((0,0),(0,28-y_eval_output.shape[1])), 'constant')
elif y_eval_output.shape[1] > 28:
y_eval_output = y_eval_output[:, :28]
num_samples = y_eval_output.shape[0]
if len(eval_id_output) != num_samples:
print(f"Adjusting IDs: had {len(eval_id_output)}, need {num_samples}")
if len(eval_id_output) > num_samples:
eval_id_output = eval_id_output[:num_samples]
else:
placeholder_ids = [f"eval_{x}" for x in range(len(eval_id_output), num_samples)]
eval_id_output = np.concatenate([eval_id_output, placeholder_ids])
eval_df = pd.DataFrame({'id': eval_id_output})
for i in range(28):
eval_df[f'F{i+1}'] = y_eval_output[:, i]
if 'output_cols' in globals():
eval_df = eval_df[output_cols]
val_df.to_csv("HOUSEHOLD_output_validation.csv", index =False)
eval_df.to_csv("HOUSEHOLD_output_evauation.csv", index =False)
Adjusting IDs: had 100, need 0
In [ ]: